# User Modeling and User Profiling:
A Comprehensive Survey

Erasmo Purificato
Otto von Guericke University Magdeburg
Magdeburg, Germany
<EMAIL_ADDRESS>
Ludovico Boratto
University of Cagliari
Cagliari, Italy
<EMAIL_ADDRESS>
Ernesto William De Luca
Otto von Guericke University Magdeburg
Magdeburg, Germany
<EMAIL_ADDRESS>
Corresponding author.

(February 21, 2024)

###### Abstract

The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.

_Keywords_ User Modeling $\cdot$ User Profiling $\cdot$ User Behavior $\cdot$
User Preferences $\cdot$ User Interests $\cdot$ User Representation

##  1 Introduction

In the pervasive era of artificial intelligence (AI) systems, the integration
of such technologies into daily life is inevitable, whether embraced
consciously or not. Specifically, within the realm of widely adopted tools,
information retrieval (IR) and recommender systems (RSs) proficiently supply
pertinent information to users in accordance with their information
requirements, personality traits, and contextual cues. Within a context where
the interaction with such systems yields a voluminous amount of personal data
on a daily basis, the imperative to discern individuals’ interests,
characteristics, and behaviors is fulfilled by user modeling and profiling
techniques [Eke et al., 2019]. These techniques primarily aim to construct a
reliable user representation (i.e., a user model or user profile) commencing
from generated data [Kanoje et al., 2015]. User modeling and user profiling
are pivotal in understanding user behavior and providing personalized
experiences. Organizations can gain valuable insights into individual
preferences and interests by analyzing user data, such as browsing history,
purchase patterns, and social interactions. This, in turn, enables the
delivery of tailored content, products, and services, enhancing user
satisfaction and engagement.

In this introduction, we will first illustrate the historical background of
user modeling and user profiling research areas. Then, we will present an
extensive overview of surveys and literature reviews on user modeling and
profiling published over time, followed by a description of the novel
contributions presented in this article. Finally, the outline of the paper
concludes the introductory section.

Figure 1: Timeline reporting the major events of the user modeling and
profiling history.

###  1.1 Historical overview

Throughout the history of scientific literature on personalization, user
modeling, and user profiling fields have constantly witnessed significant
advancements, as described by Kobsa [2001], Cena et al. [2019], and Eke et al.
[2019], among others.

Traditionally, the initial steps in this research area can be dated back to
the late 1970s when the notions of “user model” and “user modeling” were
introduced by the contributions of Allen, Cohen, Perrault, and Rich (i.e.,
[Perrault et al., 1978; Cohen and Perrault, 1979; Rich, 1979]). Their
pioneering work set the stage for the following decade’s research in this
field. It led to the creation of numerous application systems that gathered
different types of user information with varying adaptive capabilities (e.g.,
[Sleeman, 1985; Allgayer et al., 1989; Wahlster and Kobsa, 1989]). In these
early models, there was no distinct separation between the components used for
user modeling and those used for other functions. Specifically, Perrault et
al. [1978] proposed a form of modeling that aimed at understanding the beliefs
and goals of a user so that they could communicate. Interestingly, dialogues
were exploited to understand these goals. The paper’s approach to
understanding a user’s intentions using a model of the speaker’s beliefs
aligns with user modeling, where a system infers a user’s needs and
preferences from their interactions. This implies that user modeling systems
should tailor their responses based on the user’s profile to ensure effective
communication. Cohen and Perrault [1979] highlighted the need for a nuanced
representation of an agent’s mental state, including beliefs, goals, and
actions, to predict and influence behavior through communication. This
approach was crucial for developing the idea of conversant computers capable
of recognizing and generating appropriate speech acts in interaction with
users. Rich [1979] introduced one of the earliest successful user modeling
techniques by exploring the vision of using stereotypes in computer systems to
create models of individual users. Stereotypes, in this context, are
predefined sets of characteristics that are commonly associated with certain
types of users. The author discussed how these stereotypes could infer user
preferences and behaviors, allowing the system to make personalized
recommendations. The popular system Grundy was developed as a result of this
research. It acts like a librarian, suggesting novels to users based on the
stereotypes assigned to them. Based on user interactions, the system was
designed to learn and adapt its stereotypes over time to improve its
recommendations. Stereotype user modeling constituted the first attempt to
differentiate a user from other users [Schiaffino and Amandi, 2009] and
inspired several future works (e.g., [Ardissono and Sestero, 1995; Krulwich,
1997; Zimmerman and Kurapati, 2002]).

Since the late 1980s, it became apparent that the user modeling component
needed to be reusable in creating user-adaptive systems. The initial move in
this direction involved the creation of generic user modeling systems. Also
defined as user modeling shell systems by Kobsa [1990], a generic user
modeling system (GUMS) operates as an independent component within a system
during runtime, requiring developers to input it with application-specific
user modeling knowledge [Kobsa, 2001]. Introduced by Finin and Drager [1986],
the primary GUMS was never utilized with an application system, but it
established the framework for the fundamental functionality of future general
(i.e., application-independent) user modeling systems, namely providing
selected user modeling services that can be configured during development. A
few instances of these systems are: UMT [Brajnik and Tasso, 1994], TAGUS
[Paiva and Self, 1994], BGP-MS [Kobsa and Pohl, 1994], Doppelänger [Orwant,
1994], and the um toolkit [Kay, 1995]. User modeling shells were anticipated
to support intricate assumptions and sophisticated reasoning about the user,
particularly in the domains where these characteristics were identified.
Furthermore, they were expected to be adaptable for use in a broad spectrum of
other domains.

The significance of web personalization gained increasing recognition within
the domain of electronic commerce at the end of the 1990s [Hof et al., 1998;
Allen et al., 1998]. It involved tailoring product offerings, sales
promotions, product news, and ad banners to individual users based on their
navigation data, purchase history, and past interactions with the electronic
merchant. From a broader perspective, personalization facilitated the
transition from anonymous mass marketing and sales to a more individualized
one-to-one marketing approach on the Internet [Peppers and Rogers, 1993,
1997]. In this context, the involvement of user modeling was deemed crucial
[Fink and Kobsa, 2000]. Dozens of systems for web personalization, each
boasting diverse capabilities, were developed and published in that period
(e.g., [Caglayan et al., 1997; Konstan et al., 1997; Kay et al., 2002;
Brusilovsky, 2004; Kobsa and Fink, 2006]).

User profiling and user modeling research saw significant advancements in the
2000s, focusing on improving personalization and adaptability in various
systems, including adaptive hypermedia [Brusilovsky et al., 1998; Brusilovsky,
2001], a research topic emerged at the intersection of hypermedia systems
[Kobsa et al., 2001] and adaptive user interfaces [Langley, 1999]. Unlike
regular hypermedia, where all users were offered the same set of hyperlinks,
adaptive hypermedia tailored what the user was offered based on a model of the
user’s goals, preferences, and knowledge. This resulted in providing links or
content that were most appropriate to the current user [De Bra et al., 1999].
An adaptive hypermedia system tracks the user’s browsing behavior and can
change the link to a different web page or document that is more tailored to
the user. It is used in various fields such as educational hypermedia, online
information and help systems, and institutional information systems
[Brusilovsky and Maybury, 2002]. The growing interest in personalization in
online systems, seen in the mid- and end-2000s, led researchers to start
exploring ways to model user preferences to provide more tailored and relevant
content [Zhang and Koren, 2007; Berkovsky et al., 2008], such as personalized
recommendations for e-commerce websites [Braynov, 2004; Semeraro et al., 2008]
or TV shows [Zimmerman and Kurapati, 2002; Zimmerman et al., 2004; Martinez et
al., 2009]. In the same period, research also focused on creating user
profiles that accurately captured interests based on observations of user
behavior on the web (e.g., [Godoy and Amandi, 2005; Castellano et al., 2007;
Li et al., 2007, 2009; Calegari and Pasi, 2010; Gao et al., 2010; Plumbaum et
al., 2011]). The advent of the semantic web prompted investigations into
representing and modeling user preferences through ontologies [Middleton et
al., 2004; Mehta et al., 2005; Sieg et al., 2007; De Luca et al., 2010;
Sosnovsky and Dicheva, 2010; Elallioui and El Beqqali, 2012]. These ontologies
were employed to semantically organize and connect user profiles, thereby
enhancing the comprehension of user preferences and relationships.

A significant turning point in user modeling and profiling research was
characterized by the introduction of the expert finding and expert profiling
tasks within the Enterprise Track at the Text REtrieval Conference (TREC) 2005
[Craswell et al., 2005]. This was when the field started gaining considerable
attention [Eke et al., 2019]. The TREC Enterprise Track’s acknowledgment of
the necessity to delve into and comprehend expertise retrieval emphasized the
significance of these tasks [Balog et al., 2007]. This, in turn, paved the way
for a more profound investigation and comprehension of user profiling within
the realm of expert finding and expert profiling [Pavan et al., 2015; Liang
and De Rijke, 2016]. Expert finding [Balog et al., 2012] is about identifying
individuals who possess knowledge or expertise in a specific area within a
given dataset. The aim is to find the best matches for a user’s specific
request for expertise on a topic. For instance, a user might be looking for an
expert who has knowledge in a particular field, and the task would involve
searching through the dataset to find individuals who have demonstrated
expertise in that area. The challenge lies in accurately determining a
person’s expertise based on the available data and ranking these individuals
based on their relevance to the given topic. Expert profiling [Balog et al.,
2012], on the other hand, is about creating a comprehensive profile of an
individual’s expertise. It involves gathering all relevant information about a
person’s skills and knowledge from their documents. This could include their
educational background, areas of specialization, projects they have worked on,
their contributions to their field, and so on. The aim is to provide a
detailed overview of what the person knows and what they are capable of.

The 2010s marked a significant transformation in focus toward more
sophisticated user profiling methods, with a growing emphasis on
personalization in various digital services, particularly in RSs, where
researchers developed advanced algorithms to analyze user behavior and
preferences for improved content recommendations [Abel et al., 2011; Lakiotaki
et al., 2011; Masthoff, 2011; Konstan and Riedl, 2012]. Innovative approaches
involved the use of personality-based user adaptation, where automated methods
were developed for recognizing personality traits from user behaviors [Gao et
al., 2013; Gou et al., 2014; Barnett et al., 2015] and conversations [Mairesse
and Walker, 2010; Ivanov et al., 2011; Wei et al., 2017]. A notable surge in
academic literature was due to semantic user modeling techniques, which
concern the creation of computational models to understand and predict user
preferences, behaviors, and intentions based on semantic information derived
from various data sources [Aroyo and Houben, 2010; Bakalov et al., 2010;
Plumbaum et al., 2011; Cena et al., 2012; Plumbaum et al., 2012; Piao and
Breslin, 2016]. This period also saw the proliferation of mobile devices,
which led to research on user modeling in mobile and location-based contexts
[Ouanaim et al., 2010; Kuflik et al., 2012]. The integration of location data
was explored to provide context-aware strategies and recommendations. Context-
aware user modeling gained traction as researchers aimed to understand how
user preferences and behaviors change in different contexts. This included
factors such as location, time, and device, leading to more adaptive and
responsive systems [Adomavicius and Tuzhilin, 2011; Skillen et al., 2012;
Verbert et al., 2012; Said et al., 2013; Codina et al., 2015]. Concurrently,
the rise of social media platforms spurred an increased interest in
understanding and modeling user behavior within these online social
environments [Zafarani and Liu, 2013; Gou et al., 2014; Gilbert et al., 2023].
Researchers began exploring ways to incorporate social network data into user
models, applying social network analysis to understand the influence of social
connections on user preferences and behaviors [Vassileva, 2012; Zhong et al.,
2012; Zhou et al., 2012]. This included integrating social network data and
user-generated content to create more accurate and context-aware user profiles
[Piao and Breslin, 2018; Kaushal et al., 2019].

During the same period, the ascent of big data drove the investigation of
advanced data mining techniques for user modeling [Romero and Ventura, 2013;
D’Oca and Hong, 2014; van Dam and van de Velden, 2015]. Large datasets have
witnessed the application of machine learning (ML) algorithms, encompassing
clustering and classification methods, to unveil meaningful patterns and
insights into user behavior [Qi, 2010; Zghal Rebaï et al., 2013; Krishnan and
Kamath, 2017]. As users began engaging with services across diverse platforms
and devices, the emphasis turned towards formulating cross-platform user
models [Deng et al., 2013; Mercado et al., 2016; Shin, 2016; Lin et al.,
2019]. Researchers explored methods to construct cohesive user profiles that
could encompass user preferences and behaviors across different digital
environments. The proliferation of varied data types, including text, images,
and audio, spurred efforts in developing multimodal user models [Saevanee et
al., 2012, 2015; Farseev et al., 2015; Guo et al., 2018b]. These models aimed
to attain a more comprehensive understanding of user preferences and behaviors
by integrating information from various modalities. Subsequent advances in ML,
particularly neural networks, began to deeply influence this research area in
the latter half of the 2010s. From this period onwards, almost all relevant
works in this user modeling and profiling focused on the use of deep neural
networks for modeling complex user behaviors and enabling more accurate
predictions and personalized experiences [Tang et al., 2015; Żołna and
Romański, 2017; Ge et al., 2018; Ni et al., 2018; An et al., 2019; Chen et
al., 2019b; Hu et al., 2020; Wanda and Jie, 2020; Chen et al., 2021b; Fazil et
al., 2021; Yan et al., 2021; Li et al., 2022b; Yan et al., 2022; Zhao et al.,
2022; Xuan et al., 2023]. Deep learning (DL) models were also adopted to
automatically learn hierarchical representations of user preferences from raw
data, leading to improvements in recommendation accuracy [Gu et al., 2020; Wen
et al., 2021; Li et al., 2022a; Wei et al., 2022; Xue et al., 2022].

As the collection of user data became more pervasive, in recent years, there
was a growing awareness of privacy concerns, even though security and privacy
approaches for user modeling and profiling have already been proposed in the
past [Kobsa and Schreck, 2003; Schreck, 2003]. Researchers and practitioners
began exploring ways to balance the need for personalized services with user
privacy, leading to the development of privacy-preserving techniques [Aghasian
et al., 2017; Isaak and Hanna, 2018; Raber and Krüger, 2022], such as
federated user modeling [Wu et al., 2021b; Chu et al., 2022; Luo et al., 2022;
Liu et al., 2023c]. Furthermore, the increasing need for transparency and
interpretability in AI systems has resulted in a widespread surge of
scientific contributions focused on explainable AI (XAI), including its
application in user modeling [Balog et al., 2019; Huang et al., 2019; Guesmi
et al., 2022]. Efforts were directed towards enhancing the understandability
and interpretability of ML models, enabling users to gain a clearer
understanding of the construction of user models and the rationale behind
recommendations and predictions [Wang et al., 2019a; Hase and Bansal, 2020;
Xian et al., 2021; Liu et al., 2023a].

In the past few years, ethical concerns related to user modeling, particularly
focusing on bias and fairness issues, have gained significant prominence.
Addressing these ethical considerations in user profiling has become a
priority, with a commitment to fostering transparency, accountability, and
fairness in algorithmic decision-making [Balog et al., 2019; Dai and Wang,
2021; Shen et al., 2021b; Purificato et al., 2022; Zheng et al., 2022b;
Abdelrazek et al., 2023; Zhang et al., 2023b]. This transition towards a more
human-centric design and inclusive approach to user modeling underscores the
importance of recognizing diverse user demographics and preventing biases in
the modeling process. Active exploration of methods is underway to ensure that
user models maintain representativeness and fairness across different user
groups [Purificato and De Luca, 2023]. Moreover, the evolving landscape
includes a growing emphasis on fostering effective human-AI collaboration to
enhance the ethical and inclusive dimensions of user modeling [Çelikok et al.,
2023].

###  1.2 Existing surveys on User Modeling and User Profiling

Over the years, several surveys and literature reviews have been published on
user modeling and user profiling. Plenty of them targeted a specific
application domain or feature. Only a few general, comprehensive, and
exhaustive reviews and surveys have been presented.

##### Soft computing and personal information agents (2005)

Frias-Martinez et al. [2005] investigated the application of soft computing
methodologies, such as fuzzy logic, neural networks, genetic algorithms, fuzzy
clustering, and neuro-fuzzy systems, in user modeling from 1999 to 2004. The
research explores the utilization of these techniques either independently or
in conjunction with other machine learning methods. Each technique is analyzed
for its primary applications, constraints, and prospective avenues for user
modeling. Additionally, the study offers guidance on selecting appropriate
soft computing techniques based on the specific task implemented by the
application. Godoy and Amandi [2005] provided a survey of user profiling
techniques within personal information agents, exploring a range of algorithms
and methods that are pivotal for personalization and recommender systems. It
delves into collaborative filtering, text classification, user modeling, and
the application of machine learning techniques such as Naïve Bayes
classifiers, genetic algorithms, and artificial neural networks. The
effectiveness of these methods is compared, particularly in their ability to
model and adapt to user interests and preferences, with specific applications
in news filtering and email classification. The paper also examines the use of
feature selection, stemming, and various supervised learning approaches in
text classification, discussing their pros and cons and comparing different
methods like decision trees and rule-based classifiers.

##### Data mining approaches for adaptive hypermedia systems and their
applications (2006 - 2007)

Frias-Martinez et al. [2006] presented a comprehensive review of data mining
techniques applied to user modeling within the context of adaptive hypermedia
systems. It addresses the challenges involved in selecting the most suitable
data mining approach and offers guidelines for the design of user models that
leverage these techniques. The paper underscores the importance of hybrid
systems and the necessity for standardization in the field of user modeling.
Each method is thoroughly described, detailing its fundamental algorithms,
applications in user modeling, and inherent limitations. The paper provides
practical examples and explores the potential of each technique in crafting
user models for adaptive systems. Brusilovsky and Millán [2007] provided a
comprehensive overview of adaptive hypermedia and user modeling, with a focus
on personalized information access and adaptive educational systems. The study
discusses various aspects of user modeling, including the representation of
user knowledge, interests, goals, background, and individual traits, as well
as the context of the user’s work. It emphasizes the use of Bayesian Networks
(BNs) for creating dynamic and qualitative student models that integrate
expert knowledge with learning techniques, allowing for real-time improvements
in adaptive web applications.

##### Online Social Networks and Mobile Social Networks (2009 - 2013)

Benevenuto et al. [2009] published a broad analysis of user behaviors on
online social networks (OSNs) by examining detailed clickstream data from a
social network aggregator website. The study identifies two types of user
activities: “publicly visible” actions and “silent” actions, such as browsing
profiles or viewing photos, which are not immediately apparent to others. The
authors highlight the dominance of browsing activities, which constitute 92%
of all user actions, and note that including silent interactions significantly
increases the observed level of user engagement. The research extends to
multiple social networks and finds considerable variation in user behaviors
and session lengths across different platforms. Mezghani et al. [2012] delved
into the complexities of utilizing social annotations, particularly tags, for
modeling user profiles within social networks. The paper underlines the
importance of considering both static and dynamic elements when representing
user profiles to ensure they remain up-to-date and reflective of users’
evolving interests. In the context of recommendations, the paper discusses the
use of vector representations and the FOAF (“Friend of a Friend”) ontology as
methods for describing users and their interests. Remaining in the sphere of
social networks, Jin et al. [2013] provided an in-depth analysis of user
behavior modeling methods within OSNs, examining aspects such as social
connectivity, interaction patterns, traffic activity, and the dynamics of
mobile social networks (MSNs). The survey underscores the significance of
clickstream data as a tool for understanding these behaviors and acknowledges
the challenges associated with its use. A key focus of the paper is on the
security and privacy concerns prevalent in OSNs. It reviews existing
strategies to counter these threats and identifies areas where further
research is needed to develop more effective solutions.

##### Human-Computer Interaction, multi-application environments and
interoperability (2010 - 2011)

Biswas and Robinson [2010] presented a brief survey of different user modeling
techniques used in Human-Computer Interaction (HCI). After a discussion of the
historical context of user modeling, the paper delves into various approaches,
including the GOMS family of models (which stands for “Goals, Operators,
Method and Selection”), cognitive architectures, grammar-based models, and
application-specific models. Each modeling technique is examined for its
strengths and weaknesses. Additionally, the authors underscore the
significance of understanding the variety of user modeling paradigms available
to system analysts. Viviani et al. [2010] analyzed the challenges and current
research in user modeling within multi-application environments. The study
focuses on the concept of user modeling interoperability, which is essential
for cross-system personalization, and identifies two primary approaches to
achieving interoperability: standardization-based user modeling, to create
common standards for user models that can be used across different systems;
mediation-based user modeling, to translate and adapt user models between
systems that use different representations and formats by means of a mediator.
The authors emphasize the importance of developing effective user modeling
systems that can operate in a multi-application context. They acknowledge the
difficulties in achieving interoperability and suggest that hybrid solutions,
which combine elements of both standardization and mediation, may be necessary
to overcome these challenges. Another review of user model interoperability
was presented by Carmagnola et al. [2011]. The authors explored the
motivations behind interoperability and the evolution from centralized to
decentralized user model architectures. The study also addresses the
challenges associated with this research topic, including privacy concerns,
access control, encryption, and the management of conflicting data models and
values. Solutions like pseudonymous personalization, perturbation techniques,
and scrutable user models are discussed as ways to handle these challenges.

##### Demographic recommenders, learning environments, and intrusion detection
and prevention systems (2016)

Al-Shamri [2016] delivered a thorough review of various techniques and
approaches utilized in recommender systems, with a focus on demographic data,
fuzzy profiling, and similarity measures. The core of the paper is an analysis
of different user profiling methods for demographic recommender systems (DRS).
It highlights the significance of profiling in improving the performance of
these systems, noting that certain methods can lead to substantial
enhancements. The study’s findings are intended to inform the design and
implementation of DRS. Medina-Medina and García-Cabrera [2016] presented a
comprehensive examination of user modeling approaches within the context of
learning environments, particularly focusing on natural language systems,
intelligent tutoring systems, web personalization, and adaptive educational
hypermedia. The core contribution of the paper is the development of a
taxonomy for classifying user models. This taxonomy is based on criteria
related to the structure and management of user models, such as
initialization, update methods, inference techniques, and the types of
information they store. Peng et al. [2016] analyzed the role of user profiling
in intrusion detection and prevention systems (IDPS). The review delves into
the concept of behavioral profiling, which encompasses both system and user
behaviors. It discusses the use of behavioral biometrics, such as keystroke
dynamics and eye movement patterns, and psychometrics, which involve the
analysis of a user’s psychological attributes like intelligence, decision-
making patterns, and preferences. These profiling methods aim to create a
detailed user profile that can be used to detect unauthorized access or
malicious activities.

##### Entity profiling and modeling (2017)

Barforoush et al. [2017] introduced a classification framework designed to
evaluate entity profiling (EP) systems on the web, summarizing advancements in
the field from 2000 to 2015. The survey provides a set of criteria to compare
and classify EP systems, aiming to assist researchers in developing or
selecting robust systems tailored to their specific needs. EP is defined as
the process of collecting and compiling descriptive information about a
specific entity, which could be a person, organization, or location. This
information is gathered from various web sources and is used to create a
comprehensive profile that represents the entity’s characteristics, behaviors,
or relationships. Entity modeling (EM), on the other hand, refers to the
creation of abstract representations of entities, often using formal models
that define the types of entities and the possible relationships between them.
EM is crucial for understanding and organizing the information collected
during the profiling process.

##### Microblogging social networks and updated Human-Computer Interaction
models (2018)

Piao and Breslin [2018] presented a comprehensive review of user modeling
strategies for inferring user interests in microblogging social networks. It
aims to provide an overview of the state-of-the-art techniques and
methodologies used to construct and evaluate user interest profiles, which are
essential for delivering personalized content and recommendations. Similar to
previous works, Sajib Al Seraj [2018] examined user modeling approaches within
the field of HCI. The author provided an updated review of various models,
such as the GOMS family, cognitive architectures, grammar-based models, and
application-specific techniques. Among the discussed future challenges, the
paper highlights the significance of user control in intelligent systems and
raises concerns about privacy and security in relation to user models.

##### Wearable, ubiquitous and mobile computing technologies (2019)

Cena et al. [2019] provided an in-depth analysis of the evolution of user
modeling, particularly with the advent of wearable and ubiquitous computing
technologies. The authors introduce the concept of Real World User Model
(RWUM), which integrates data from a person’s everyday life, significantly
expanding the scope and depth of traditional user models. Zhao et al. [2019b]
presented a survey on user profiling through the analysis of smartphone
application usage, including the types of data that can be collected, the
methods employed for profiling, and the potential applications and
implications of this practice. The paper discusses how personal information
such as demographic attributes, personality traits, psychological status,
personal interests, and lifestyles can be inferred from smartphone app data.
It also examines the use of other data sources, such as online social networks
and call detail records (CDRs), for effective user profiling.

##### Representation learning and review-based user profiling (2020 - 2021)

Li and Zhao [2020] published a detailed analysis of the latest developments in
user modeling, with a particular focus on representation learning techniques.
The study categorizes the methods into two main types: static and sequential
representation learning. Static representation learning methods include matrix
factorization and deep collaborative filtering, which are used to capture user
preferences and item characteristics in a static context. These methods are
foundational to many recommender systems and are crucial for understanding
user behavior. Sequential representation learning methods, such as recurrent
neural networks, are discussed as they pertain to capturing the dynamic nature
of user preferences over time. These methods are particularly relevant for
applications that require understanding the temporal aspects of user behavior.
Dong et al. [2021] presented a systematic mapping study on review-based user
profiling (RBUP), an area of research that utilizes user-generated reviews to
create detailed user profiles. The study aims to provide an overview of the
current state of the field, its evolution, publication trends, application
areas, and co-authorship patterns. The research process for RBUP involves
formulating research questions, conducting systematic literature searches
using keywords, "snowball" searching, and applying inclusion and exclusion
criteria to select relevant papers. The analysis of the selected papers
includes bibliometric analysis, examining the time distribution of
publications, identifying paper venues, and exploring co-author networks.

##### Recommender systems (2022)

Tenemaza [2022] highlighted the critical role of user modeling in the
development of recommender systems. The work delves into the various
characteristics and structures that constitute user models, examining the
diverse range of data sources and attributes that are employed to represent
and understand users. It also points out a significant gap in the current
research landscape: the lack of a generalized user model, conveying that
existing studies tend to focus on specific components of user modeling rather
than adopting a holistic approach that integrates all relevant aspects.

##### User behavior modeling with Deep Learning, Long Short-Term Memory
networks, and Large Language Models (2022 - 2023)

In the same context of recommender systems, He et al. [2023] presented a
survey of user behavior modeling. The paper discusses the deep network
structures and techniques used to capture behavior dependencies, including the
handling of long user behavior sequences and the incorporation of different
behavior types. It also explores the challenges and advancements in user
behavior modeling, such as the use of long-term behavior histories, multi-type
behaviors, and side information. Sudhakar et al. [2022] offered a thorough
study on the application of Long Short-Term Memory (LSTM) networks for
generating and analyzing web user profiles. The primary objective is to create
detailed and personalized user profiles that can enhance the customization of
applications. The research encompasses several stages, including data
collection, preprocessing, feature extraction, and the classification process,
all of which are critical for the effective implementation of LSTM in user
profiling. A significant portion of the paper is dedicated to comparing the
LSTM-based method with existing user profiling techniques, concluding by
underscoring the potential of LSTM networks in revolutionizing the field of
user profiling and providing a pathway for more personalized and effective web
experiences. Tan and Jiang [2023] provided an overview of the use of Large
Language Models (LLMs) in the field of user modeling and recommender systems.
The article highlights the potential of LLMs in understanding user behavior
and generating personalized recommendations. It also delves into the
challenges associated with LLM-based User Modeling, such as detecting
suspicious behavior like fraud and misinformation and addressing privacy and
security concerns. It emphasizes the need for comprehensive benchmarks,
evaluation criteria, trustworthy user modeling, fairness, and efficient domain
adaptation.

##### General comprehensive surveys (2009 - 2022)

Schiaffino and Amandi [2009] discussed the key components of user profiles
adopted in that period for intelligent agents, adaptive systems, intelligent
tutoring systems, recommender systems, e-commerce applications, and knowledge
management systems. The paper also discusses several intelligent user
profiling techniques, including stereotypes, Bayesian networks, association
rules, and case-based reasoning. Cufoglu [2014] examined the advantages and
disadvantages of existing user profiling methods and explored the potential of
these methods for enhancing service personalization. A key part of the paper
is the analysis of classification and clustering algorithms, which are pivotal
in the construction of user profiles. The author conducts simulations using
real-world user profile data to evaluate the performance of various algorithms
in this context. The survey presented by Kanoje et al. [2015] delves into the
significance of user profiling and examines the progression, methodologies,
and practical applications across various sectors, including academic
literature recommendations, e-tourism, energy conservation, and employment
matchmaking. The article outlines a range of user profiling techniques, such
as implicit profiling, perceptual preference questionnaires, social profiling,
weakly supervised extraction, ontological approaches, and the use of implicit
feedback. Farid et al. [2018] explored the essential information required for
constructing diverse user models, the methods employed for collecting this
information, the representation and maintenance of the user model, and,
ultimately, the utilization of the user model to provide personalized
services. A classification schema for user profiling research is proposed, and
the paper also looks at how user profiling is applied in areas like
personalized web search, recommender systems, adaptive learning,
visualization, and personalized services in online social networks. The most
recent general and comprehensive survey on user modeling and user profiling
has been provided by Eke et al. [2019]. This research paper presents an
extensive review that focuses on the methodologies, models, and processes
involved in constructing accurate user profiles for service personalization.
The authors aim to fill a gap in the existing literature by examining user
profiling from the perspectives of data acquisition, feature extraction,
modeling techniques, and performance evaluation. The paper discusses both
static and dynamic user profiles, the process of user modeling, and the
construction and updating of user profiles. It delves into the methods of user
information collection, data preprocessing, and feature extraction, providing
examples of studies that have utilized these techniques in user profiling.
Various approaches to user profiling are explored, including collaborative and
hybrid filtering, statistical models, neighborhood-based techniques, machine
learning, user ontology, and filtering methods. For the sake of completeness,
in chronological order, it is worth mentioning two other articles, published,
however, with slightly different purposes than to provide a complete overview
of user modeling and profiling features. Abri et al. [2021] proposed a
classification of the major dimensions of user models. The paper briefly
explores the user profiling process, detailing the methods for collecting user
information, such as browsing history, search queries, and user feedback. It
examines learning techniques for constructing user profiles, including rule
induction and predictive statistical models. Recently, Tchuente [2022]
conducted a bibliometric analysis of the field of user modeling and user
profiling within information systems, utilizing a dataset of 52 027
publications. The study aims to map out the landscape of research in this area
by identifying key authors, publication sources, institutions, and countries,
as well as examining their collaborative efforts. The article also analyzes
the main research topics and their respective subtopics, outlining the
evolution of the field and identifying current trends. It further discusses
potential future research directions and highlights existing gaps in the
literature. This provides valuable insights for academics, researchers, and
industry professionals interested in user modeling and profiling.

###  1.3 Scope and novel contributions of the survey

The scientific literature has long devoted considerable attention to the
research areas of user modeling and user profiling, as illustrated in the
previous sections. Across the years, tons of papers and numerous surveys on
these topics have been published. Our survey stems primarily from a number of
key issues that emerged from the study of the literature: (1) the absence of
an article including a wide historical overview and general taxonomy; (2) the
ambiguous use of basic core terminology (i.e., “user modeling”, “user model”,
“user profiling”, and “user profile”); and (3) the lack of a comprehensive and
up-to-date overview of this research area since 2019, to fill the gap due to
the publication date of the most recent exhaustive survey (i.e., [Eke et al.,
2019]).

Specifically, the novel contributions presented by our survey are listed and
briefly described below, with the indication of the section including the
related content:

  * •

We provide a complete outline of the large, long-standing, and ever-growing
research fields of user modeling and user profiling (Section 1), by offering a
detailed historical overview (Section 1.1) and an inspection of the literature
reviews published across the decades (Section 1.2). After retracing the most
significant milestones of the literature, starting from the stereotype user
modeling, introduced in 1979, to the recent studies on beyond-accuracy
perspectives, a formal taxonomy is proposed (Section 4), taking into account
all the currently active topics in the research area, including the trends
that emerged in the last few years.

  * •

We examine the definitions associated with each key term in this research
domain (i.e., “user modeling”, “user model”, “user profiling”, and “user
profile”), aiming to eliminate ambiguity and confusion in their usage and
proposing new, encyclopedic, and easily understandable definitions (Section
2).

  * •

We present in-depth the paradigm shifts that have occurred in recent years,
especially due to technological evolution, as well as the current research
directions and novel trends in the field (Section 3). In particular, we
illustrate and discuss the advances in the following research topics:

    * –

Implicit vs. explicit user profiling (Section 3.1): in recent years, studies
have adopted almost solely implicit (or hybrid) approaches for data
collection. True explicit profiling (i.e., user data retrieved from surveys or
questionnaires) is replaced by a sort of pseudo-explicit profiling, where the
explicit information is taken from public data already shared by the users for
different purposes (e.g., social network accounts).

    * –

User preferences and interests (Section 3.2): the rise and daily use of
digital platforms, such as e-commerce services and RSs of all kinds, have
inevitably led to a steady increase in information about users’ preferences
and interests. This phenomenon is obviously reflected in the context of user
modeling and profiling research.

    * –

User behavior modeling (Section 3.3): the examination of user behaviors has
evolved over time, bringing forth novel concepts, such as micro and macro
behavior modeling (i.e., respectively, the immediate actions that a user takes
reflecting short-term preferences, and large-scale actions that reflect a
user’s long-term commitment), multi-behavior modeling (i.e., the practice of
integrating diverse forms of user interactions with items, as opposed to
depending on a single type), sequential behavior modeling (i.e., considering
the temporal sequences of behaviors influencing user interests), hierarchical
user profiling (i.e., a technique used in personalized recommender systems,
particularly in e-commerce, to model users’ real-time interests at different
levels of granularity), and mobile user profiling (i.e., the effort of
discerning users’ interests and behavioral patterns from their mobile
activities).

    * –

User representation (Section 3.4): several works have underlined the absence
of a generalized user model, suggesting that current research tends only to
concentrate on particular facets of user modeling. This led to the advent and
diffusion of the concepts of universal user representation and holistic user
modeling.

    * –

Evaluation (Section 3.5): currently, there are two main lines for evaluating
the performance of a standalone user profiling approach, i.e., assess the
proposed model or method based on the effectiveness of a classification task
at predicting a user’s personal characteristics, or generate simulated data to
aid in minimizing the volume of gathered user data while maintaining profiling
efficiency and safeguarding the privacy and confidentiality of users’ personal
information.

    * –

Graph data structures (Section 3.6): as in many other domains, also in user
modeling and profiling, extensive attention has been given to exploring and
adopting graph structures (including knowledge graphs), particularly in the
context of online social media.

    * –

Deep learning (Section 3.7): the advent of deep neural network-based models
has ushered in a transformative era across various domains, and user modeling
and profiling have not been exceptions. We discuss contributions that employed
different deep learning architectures and models, i.e., differentiable user
models, attention mechanism, graph neural networks, convolutional neural
networks, autoencoders, recurrent neural networks, long-short term memory, and
transformers.

    * –

Beyond-accuracy perspectives (Section 3.8): as for deep learning
architectures, the adoption of “beyond-accuracy” techniques signifies a
pivotal transition in every domain, including user modeling and profiling.
These approaches go beyond predictive precision, prioritizing values like
privacy, fairness, and explainability. In user modeling, it ensures accurate
predictions while safeguarding user privacy, addressing biases, and promoting
transparency.

    * –

Application domains (Section 3.9): user modeling and profiling are finding
novel applications in critical domains, addressing contemporary challenges. In
the realm of fake news detection, these techniques analyze user behavior and
content interactions to identify and combat misinformation. In social
networks, even if not an unexplored domain, user profiling enhances community
engagement and content recommender systems by understanding individual
preferences. Cybersecurity benefits from user modeling through anomaly
detection, providing a proactive approach to identifying potential threats. In
Massive Open Online Course (MOOC) platforms, user modeling tailors educational
content, ensuring a personalized learning experience for each student.

  * •

We discuss the findings of our survey, underlining the concepts that hold over
time in the field, along with the emerging future research directions (Section
5).

##  2 Analysis of the Terminology

In this section, we examine the interpretations tied to each significant term
related to the research fields studied, with the intention of removing any
uncertainty or misinterpretation in their use. Furthermore, we put forth a
novel, general, and easily comprehensible set of definitions. Particularly,
after selecting specific contributions where precise characterizations were
provided, we methodically offer a series of definitions for the terms “user
profile”, “user model”, “user profiling”, and “user modeling”. Our
consideration encompasses both widely accepted and recently formulated
definitions, with a focus on capturing the most contemporary meaning of each
term in question.

User profile, the first term to get a formal and precise definition together
with user model, has been described as:

  * •

a representation of the preferences of any individual user; roughly, it is a
structured representation of the user’s needs through which a retrieval system
should, e.g., act upon one or more goals based on that profile and
autonomously, pursuing the goals posed by the user [Amato and Straccia, 1999].

  * •

the narration of a user’s behavior, interests, characteristics, and
preferences obtained through interviews and questionnaires or dynamically with
the aid of machine learning algorithms and data mining techniques [Godoy and
Amandi, 2005].

  * •

the application of ontology for the systematic representation of the user’s
interest; it enables the conceptual representation of the knowledge that
constitutes user preference and context [Calegari and Pasi, 2010].

  * •

the information that offers insight into a user’s need and predicts his future
intention; this information depends on similarities, trace handling, and
prediction through ML [Alaoui et al., 2015].

  * •

the procedure for gathering information on the user’s interest; the system
utilizes such information to tailor services and improve the user’s
satisfaction [Kanoje et al., 2015].

  * •

a set of information that describes a user; it consists of demographic
information, such as the user’s name, age, country, and level of education,
which represents user preferences or interests in either a single or group of
users [Ouaftouh et al., 2015].

  * •

a pattern that consists of user behavioral tendencies and preferences; the
user profile knowledge acquired provides an idea of the user’s behavior
knowledge and can predict his/her intentions [Lashkari et al., 2019].

  * •

a collection of user interests, characteristics, behaviors, and preferences,
and also a system for collecting, organizing, and guessing user information
[Sudhakar et al., 2022].

User model has been defined as:

  * •

a representation of information about an individual user that is essential for
an adaptive system to provide the adaptation effect [Brusilovski et al.,
2007].

  * •

user’s information for effective adaptation in an adaptive system; for
instance, it facilitates prioritizing an adaptive selection of
important/relevant items for users when searching for relevant data [Gao et
al., 2010].

  * •

a data structure that is used to capture specific characteristics about an
individual user [Piao and Breslin, 2018].

  * •

a representation of the knowledge and preferences of users; it is not a
mandatory part of the software, but it helps to get the system to serve the
user better. Any information stored about the user or usage pattern is not a
user model unless it can be used to get some explicit assumption about the
user [Sajib Al Seraj, 2018].

  * •

a summary of the user’s interests, characteristics, behaviors, or preferences
[Tchuente, 2022].

Moving to the operations, the definitions provided for user profiling depict
it as:

  * •

the process of acquiring, extracting, and representing the features of users
[Zhou et al., 2012].

  * •

the process to produce an accurate user information representation, usually
stored in the user profile [De Campos et al., 2014].

  * •

a means of determining the user’s interest data that is built upon the
knowledge of the user and the accurate system’s retrieval of user satisfaction
[Kanoje et al., 2015].

  * •

the task of inferring user personality traits from user-generated data [Chen
et al., 2019b].

  * •

the process of inferring an individual’s interests, personality traits, or
behaviors from generated data to create an efficient user representation,
i.e., a user model, which is exploited by adaptive and personalized systems
[Eke et al., 2019].

  * •

the efforts of extracting a user’s interest and behavioral patterns from
users’ activities [Wang et al., 2019b].

  * •

the process of automatically converting user information into a predefined and
interpretable format that reflects the most important aspects of the user’s
profile, which are useful for further decision-making in practical
applications [Vo et al., 2021].

User modeling has been defined as:

  * •

the process of gathering information about a user’s interests, constructing,
maintaining, and using user profiles [Farid et al., 2018].

  * •

the practice of capturing the latent interests of the user and deriving the
adaptive representation for each user [Ren et al., 2019].

  * •

the process of building up and modifying a conceptual understanding of the
user. Its task is to learn a latent representation for each user, with the
help of items, item features, and/or user-item response matrix, with
applications to response prediction, recommendation, and others [Li and Zhao,
2020].

  * •

the process of capturing, recording, and managing user needs and interests by
creating a user profile [Abri et al., 2021].

  * •

the process of obtaining the user profile, which is a conceptual understanding
of the user for personalized recommender systems. The key idea is to learn the
representation for each user by leveraging the user’s interacted items or the
features of the items, and the obtained representations are used for a wide
range of applications such as response prediction and recommendation [Kim et
al., 2023].

Originally, another term was also used in research papers (not for long time),
i.e., user profile modeling, which has been described as:

  * •

the process that constitutes the methodology for building a user profile; it
requires two steps to describe: “what” has to be represented, and “how” this
information is effectively represented [Amato and Straccia, 1999].

Upon examining the provided definitions, it becomes apparent that in the
literature, particularly when considering articles from the last decade, the
terms “user model” and “user profile” (along with “user modeling”, “user
profiling”, and “user profile modeling”) exhibit conceptually overlapping
descriptions. Consequently, we can assert definitively that these two terms
can be utilized interchangeably, and within the scientific literature
reviewed, they can be regarded as synonymous. Hereinafter, in our survey, we
will use just one term directly, without explicitly specifying the other. In
this scenario, bringing together the peculiarities of the provided
characterization, we propose two novel, comprehensive, encyclopedic
definitions:

  * •

A user model (or user profile) is a structured representation of an individual
user’s preferences, needs, behaviors, and demographic details to personalize
system interactions. It is derived from direct user feedback or inferred
through machine learning and data mining techniques. It supports the
predictions of future user intentions and the refinement of systems response
to enhance user satisfaction. User models are often instrumental in optimizing
the relevance and efficiency of adaptive systems, ensuring that user
interactions are aligned with individual needs and preferences.

  * •

User modeling (or user profiling) is the process of acquiring, extracting, and
representing user features and personal characteristics to build accurate user
models (or user profiles). It encompasses inferring personality traits and
behaviors from user-generated data. This dynamic practice includes
automatically converting user information into interpretable formats,
capturing latent interests, and learning conceptual user representations.
Essentially, user modeling constitutes the methodology for building and
modifying user models, determining “what” to represent and “how” to
effectively represent this information for adaptive and personalized systems.

##  3 Paradigm Shifts and New Trends

As explained in Section 1.3, one of the driving factors behind the creation of
this survey is the absence of a thorough and contemporary literature review on
the subject of user modeling. The most recent general survey in this research
field is dated back to 2019 (i.e., [Eke et al., 2019]), and considers
scientific contributions up until December 2018111The paper by Lashkari et al.
[2019] cited within the survey is dated 2019, but it was officially published
on September 22, 2018, as reported on the publisher website
(https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5321)..
To be accurate, a few articles on the subject were published after that date,
but they cannot be regarded as complete surveys. They offer a bibliometric
analysis [Tchuente, 2022] and a concise, non-exhaustive categorization of user
model dimensions [Abri et al., 2021].

In this section, we offer a thorough exploration of the recent paradigm
shifts, particularly influenced by technological advancements, along with the
current research orientations and emerging trends in the field.

###  3.1 Implicit and explicit user profiling

Traditionally, early profiling methods focused solely on examining static
attributes. Explicit user profiling (also referred to as static profiling or
factual profiling) required direct input from the user, such as filling out a
questionnaire, completing an online form, providing ratings, or explicitly
stating preferences (e.g., [Raghu et al., 2001; Poo et al., 2003; Zigoris and
Zhang, 2006; Brusilovski et al., 2007]). Relying exclusively on explicit
profiling quickly became problematic as users were reluctant to disclose their
information due to privacy concerns, or found the form-filling process to be
cumbersome and avoided it. Consequently, the accuracy of this type of
profiling diminished over time [Kanoje et al., 2015; Kasper et al., 2017].

In this context, although the two approaches were used simultaneously even in
the past years, contemporary systems shifted their view by placing greater
emphasis on implicit user profiling (also known as behavioral or adaptive
profiling) [Kanoje et al., 2015; Eke et al., 2019], which entails the passive
collection and analysis of dynamic user data, such as observing user behavior,
interactions, and preferences without requiring direct user input (e.g.,
[Kulkarni et al., 2019; El-Ansari et al., 2020; Qian et al., 2021; Ma et al.,
2021; Gu et al., 2021c; Bedi et al., 2022; Han et al., 2022; Yan et al.,
2022]).

However, at the same time, the use of static data for user profiling has
turned into gathering explicit information derived from public data that users
have previously shared for different purposes, such as the creation of social
network accounts (e.g., [Shu et al., 2018, 2019]) or the usage of travel
platforms (e.g., [Zhang et al., 2020]). For this emerged category, we
introduce the term pseudo-explicit user profiling.

###  3.2 User preferences and interests

In the realm of user modeling, the evolution in exploring user preferences and
interests has closely mirrored the advancements in implicit and explicit
profiling. Initially, user preferences and interests have been modeled using
explicit and direct feedback (e.g., [Amatriain et al., 2009; Lakiotaki et al.,
2011; Kellner and Berthold, 2012; Fu et al., 2013]).

In recent times, the increasing prevalence and everyday utilization of digital
platforms, including e-commerce services and various recommender systems, as
well as the hesitancy of users in providing direct feedback, led to an
abundance of user-generated data, such as social interactions and opinionated
text content, and to a subsequent growing emphasis of research studies on
capturing user interests and preferences hidden in users’ historical behaviors
(e.g., [Guo et al., 2018a; Cami et al., 2019; Kulkarni et al., 2019; Logesh et
al., 2019; Majumder et al., 2019; Greco and Polli, 2020; Hassan et al., 2021;
Kostric et al., 2021; Lu et al., 2021; Olaleke et al., 2021; Vo et al., 2021;
Yilma et al., 2021; Zhang and Challis, 2021; Curmei et al., 2022; Fan et al.,
2022; Gomez Bruballa et al., 2022; Ma et al., 2022a; Ariannezhad et al., 2023;
Sguerra et al., 2023; Wang et al., 2023]).

Also worth mentioning, in the area of preference modeling, is the concomitant
increase in specific studies pertaining to short- and long-term preference
modeling (e.g., [An et al., 2019; Guo et al., 2019; Wu et al., 2019b; Yu et
al., 2019; Hu et al., 2020; Sun et al., 2020; Fazelnia et al., 2022; Liu et
al., 2023b]).

###  3.3 User behavioral modeling

The examination of user behaviors has significantly evolved to include a
variety of sophisticated modeling techniques that provide a deeper
understanding of user behavior in various contexts.

##### Micro and macro behavior modeling

Micro behavior modeling refers to the analysis of immediate actions taken by a
user, which reflect their short-term preferences. These actions might include
clicks, views, or interactions with specific components on a webpage or app.
Macro behavior modeling, on the other hand, looks at large-scale actions that
indicate a user’s long-term commitment or patterns, such as purchase history
or subscription renewals. Examples of contributions on these topics are
provided by Gu et al. [2020] and Wen et al. [2021].

##### Multi-behavior modeling

This approach integrates various forms of user interactions with items, rather
than relying on a single type of interaction. Multi-behavior modeling
acknowledges that users often engage with platforms in multiple ways (such as
clicking, favoriting, reviewing, and purchasing), and each of these behaviors
can provide valuable insights into their preferences and intentions, and
improve the performance of personalized recommender systems (e.g., [Jin et
al., 2020; Xia et al., 2021a, b; Zhang et al., 2022; Cheng et al., 2023; Cho
et al., 2023; Xuan et al., 2023]).

##### Sequential behavior modeling

Sequential behavior modeling takes into account the order and timing of user
actions, recognizing that the sequence of behaviors can influence a user’s
interests. This temporal aspect is crucial for services like online shopping,
news feeds, and advertising, where the sequence of user interactions can
reveal evolving preferences and help in predicting future actions (e.g., [Ren
et al., 2019; Yuan et al., 2020a; Bian et al., 2021; Cao et al., 2022; Chen et
al., 2022a]).

##### Hierarchical user profiling

Hierarchical user profiling, integral to personalized recommender systems in
e-commerce, models users’ real-time interests at various levels of
granularity. This approach enables a comprehensive understanding of both
immediate and long-term user preferences, crucial for accurate
recommendations. Examining user behaviors hierarchically provides nuanced
insights, particularly beneficial for predicting conversion rates in
e-commerce platforms. This method enhances the precision of recommendations by
delving into user preferences and behaviors, contributing to a tailored and
effective user experience in the dynamic e-commerce landscape. Examples of
works presenting hierarchical user profiling approaches can be found in Gu et
al. [2020], Wen et al. [2021], Li et al. [2022a], Wei et al. [2022], and Xue
et al. [2022].

##### Mobile user profiling

Mobile user profiling involves discerning users’ interests and behavioral
patterns based on their activities on mobile devices. Given the ubiquity of
smartphones and the wealth of data they generate, mobile user profiling is
increasingly important for delivering personalized content and services that
align with users’ on-the-go lifestyles. Contributions in this topic are
provided by Bhogi et al. [2019], Wang et al. [2019b, 2020b, 2021b], and Zhao
et al. [2022].

###  3.4 User representation

Different studies have highlighted the scarcity of studies on generalized user
model representation (e.g., [Tenemaza, 2022]), pointing out that current
research tends to focus on specific aspects of user modeling rather than a
holistic approach. This observation has led to the development and widespread
adoption of concepts such as universal user representation and holistic user
modeling. The shift towards these inclusive modeling approaches allows
researchers and practitioners to better understand and cater to the complex
and multifaceted nature of users in digital environments.

##### Universal user representation

Universal user representation is an emerging concept that aims to create a
unified and generalized profile of a user by encapsulating a broad spectrum of
user behaviors and preferences without bias towards any specific task. This
approach is designed to be applicable across various domains and applications,
providing a more complete understanding of users that can be leveraged for
multiple purposes, such as user profiling and preference prediction (e.g., [Ni
et al., 2018; Yuan et al., 2020a; Gu et al., 2021b; Yuan et al., 2021; Kim et
al., 2023]).

##### Holistic user modeling

Holistic user modeling takes this idea further by integrating diverse and
heterogeneous personal data sources, such as social networks and personal
devices, to construct a comprehensive representation of the user. This model
seeks to capture the full range of user characteristics and behaviors,
providing a more nuanced and complete picture of the user that can be used to
personalize experiences and interactions across different platforms and
services (e.g., [Gong and Wang, 2018; Musto et al., 2018, 2020a, 2020b, 2020c,
2021; Gong et al., 2020]).

###  3.5 Evaluation

There are currently two primary strategies for evaluating the performance of
standalone user profiling methods.

The first approach involves assessing the proposed model or method based on
its effectiveness in a classification task, which aims to predict a user’s
personal characteristics. This method often involves using machine learning
techniques to classify user profiles as genuine or not (e.g., [Costanzo et
al., 2019; Chen et al., 2021b; Dai and Wang, 2021; Yan et al., 2021]).

The second approach involves generating simulated data to help reduce the
amount of user data collected while maintaining the efficiency of profiling
and protecting the privacy and confidentiality of users’ personal information
(e.g., [Zerhoudi et al., 2022; Balog and Zhai, 2023]).

###  3.6 Graph data structures

In the field of user modeling, much like in many other domains, there has been
a significant focus on the exploration and implementation of graph structures,
including knowledge graphs. This is particularly evident in the context of
online social media.

Graph structures are powerful data representations that capture relationships
among data objects, making them ubiquitous in real-world applications. In the
context of user modeling, graph structures can be used to represent and
analyze user behavior, preferences, and interactions (e.g., [Guo et al.,
2018a; Wang et al., 2019b; Wanda and Jie, 2020; Wang et al., 2021b; Chen et
al., 2022b; Guan et al., 2022; Yang et al., 2022; Liu et al., 2023a; Yang et
al., 2023]).

Knowledge graphs, a specific type of graph structure, have been recognized for
their ability to effectively represent complex information, thereby gaining
the attention of both academia and industry. They accumulate and convey
knowledge of the real world, making them particularly useful in the context of
social media, where they can be used to analyze critical information from
people’s activities and posts. For example, knowledge graphs can be used to
recommend accurate content that interests users and to connect users with
persons of interest. Examples of contributions leveraging knowledge graphs for
user modeling can be found in Huang et al. [2019], Wang et al. [2020b], Anelli
et al. [2021], Wang et al. [2021a], and Xuan et al. [2023].

###  3.7 Deep learning

The advent of deep neural network-based models has sparked a revolutionary
shift across various fields, including user modeling. These models have
significantly contributed to advancements in this field, enabling more
accurate and comprehensive user profiling and prediction of user behavior.
Different deep learning approaches and architectures have been employed in
user modeling. In particular, differentiable user models (e.g., [Hämäläinen et
al., 2023]), attention mechanism (e.g., [Wang et al., 2020c, 2022a; Fazil et
al., 2021; Qi et al., 2021, 2022b; Chu et al., 2022]), graph neural networks
(e.g., [Chen et al., 2019b, 2021b; Dai and Wang, 2021; Wu et al., 2021a; Yan
et al., 2021, 2022; Han et al., 2022; Zhao et al., 2022; Cheng et al., 2023]),
convolutional neural networks (e.g., [Wang et al., 2019b; Wanda and Jie, 2020;
Fazil et al., 2021; Qi et al., 2022b]), autoencoders (e.g., [Abu Sulayman and
Ouda, 2019; Wang et al., 2019b; Fazelnia et al., 2022; Liu et al., 2023c]),
recurrent neural networks (e.g., [Ge et al., 2018; Ni et al., 2018; Yu et al.,
2019; Gu et al., 2020; Chu et al., 2022; Li et al., 2022b]), long-short term
memory (e.g., [Żołna and Romański, 2017; Ma et al., 2020; Fazil et al., 2021;
Sahoo and Gupta, 2021; Nkambou et al., 2023]), and transformers (e.g., [Gu et
al., 2021a; Kota et al., 2021; Zhu et al., 2021; Avny Brosh et al., 2022; Wu
et al., 2022; Zheng et al., 2022a]).

###  3.8 Beyond-accuracy perspectives

Similar to the transformation observed in deep learning, the incorporation of
advanced techniques extending beyond mere accuracy marks a significant shift
in various domains, with a particular impact on user modeling and profiling.
These approaches transcend the conventional pursuit of predictive precision
and instead prioritize fundamental values such as privacy, fairness, and
explainability. In our domain, these methods ensure not only precise
predictions but also the protection of user privacy (especially through
federated learning approaches, e.g., [Wu et al., 2021b; Chu et al., 2022; Luo
et al., 2022; Liu et al., 2023c; Zhang et al., 2023a]), the detection and
mitigation of biases (e.g., [Dai and Wang, 2021; Shen et al., 2021b;
Purificato et al., 2022; Zheng et al., 2022b; Abdelrazek et al., 2023;
Purificato and De Luca, 2023; Zhang et al., 2023b]), and the promotion of
transparency and interpretability (e.g., [Balog et al., 2019; Huang et al.,
2019; Hase and Bansal, 2020; Xian et al., 2021; De Pauw et al., 2022; Guesmi
et al., 2022; Minn et al., 2022; Ding et al., 2023]).

###  3.9 Application domains

User modeling is being innovatively applied in various important research
fields to tackle contemporary challenges.

In the context of fake news detection, user modeling techniques are employed
to scrutinize user behavior and their interactions with content. This analysis
aids in the identification and mitigation of misinformation, thereby enhancing
the credibility of the information ecosystem. Examples can be found in Shu et
al. [2018, 2019], Monti et al. [2019], Sahoo and Gupta [2021], and Allein et
al. [2023].

Social networks, although their domain is not entirely unexplored for user
modeling, greatly benefit from it by comprehending individual preferences for
enriching community engagement and refining content recommender systems (e.g.,
[Kaushal et al., 2019; Gilbert et al., 2023]), or detecting fake profiles and
bots (e.g., [Wanda and Jie, 2020; Fazil et al., 2021]).

In the field of cybersecurity, user modeling contributes significantly through
anomaly detection. It offers a proactive strategy to spot potential threats by
identifying unusual patterns in user behavior, thereby bolstering security
measures (e.g., [Lashkari et al., 2019; Kwon et al., 2021]).

Lastly, in the education sector, particularly on massive open online course
(MOOC) platforms, user modeling is used to customize educational content. It
ensures that each student receives a learning experience tailored to their
unique needs and learning style, thereby enhancing the effectiveness of online
education. Examples are seen in Sunar et al. [2020], Sanchez-Gordon et al.
[2021], and Jin [2023].

Figure 2: Taxonomy of the reviewed literature and trends for user modeling.
The Modeling techniques tree is detailed in Figure 3.

##  4 Current Taxonomy

The taxonomy presented in this section arises from the necessity to offer a
renewed and contemporary reference in user modeling research. This aims to
benefit present researchers and serve as a valuable resource for individuals
who will engage in the study of this subject in the upcoming times. The
categorization proposed in our survey takes inspiration from the similar
contributions published over the years and described in Section 1.2
(especially [Schiaffino and Amandi, 2009; Kanoje et al., 2015; Farid et al.,
2018; Eke et al., 2019]), to make sure to maintain continuity in the
literature. Figure 2 shows the formal taxonomy we put forth. It considers all
presently active topics within the research area and incorporates the trends
that have surfaced in recent years.

###  4.1 User profile content

User profiles typically hold details about an individual in a specific system
or application, incorporating diverse content to shape and personalize the
user experience. This content usually falls into two categories: static, which
provides foundational information, and dynamic, offering real-time updates and
interactivity. This distinction highlights how content behaves and its
frequency of change, creating a complete user representation.

####  4.1.1 Static content

Static content refers to information that does not change frequently or
automatically [Poo et al., 2003]. It is consistent across user sessions and
does not adapt in real-time to user interactions [Eke et al., 2019]. Static
content is typically set by the user during account creation or through
profile settings and remains the same until the user decides to update it
manually [Schiaffino and Amandi, 2009]. Examples of static content in a user
profile include: demographic data, background, knowledge, skills, goals,
needs, and personality traits.

##### Demographic data

The demographic characteristics of a user include basic features such as name,
country, gender, age, native language, education, family members, and more.
Dong et al. [2017] presented a study on user demographic and profile modeling
within the context of large-scale mobile communication networks. It focuses on
the prediction of demographic attributes such as gender and age by analyzing
users’ online behaviors, including browsing, gaming, and search activities, as
well as their social decisions and communication patterns. Solomon et al.
[2018] proposed an approach for predicting demographic attributes of cellphone
users by analyzing their GPS data. The authors focus on extracting mobility
profiles and location traces to infer characteristics such as age, gender,
marital status, and academic affiliation. Zhang et al. [2020] introduced a
method for simulating the daily electric vehicle (EV) charging load profiles
by incorporating the demographics and social characteristics of vehicle users,
such as gender, age, and education level. The study emphasizes that these user
attributes significantly influence the magnitude and peak times of EV charging
loads. Rozen et al. [2021] presented an approach to predicting the demographic
characteristics of online users by analyzing their comments on news articles.

##### Background, knowledge, and skills

Background data, containing information about a user’s educational,
professional, and personal background, including their field of study, work
experience, and cultural background, is crucial for creating wide user
profiles. Knowledge data refers to a user’s expertise, domain knowledge, or
specific knowledge related to a system or platform, and may include details
about their educational qualifications, certifications, or areas of expertise.
Additionally, skills data pertains to a user’s competencies and abilities,
covering both technical and soft skills such as communication, problem-
solving, and leadership abilities. This diverse set of background, knowledge,
and skills data is instrumental in personalizing user experiences, enabling
adaptive interactions, and informing the development of tailored training and
educational applications (e.g., [Brusilovsky and Millán, 2007; Schiaffino and
Amandi, 2009; Guo et al., 2018a; Li and Zhao, 2020]). To provide some specific
and recent cases, Minn et al. [2022] presented a method for student modeling
called Interpretable Knowledge Tracing (IKT). This approach is designed to
predict student performance by leveraging three meaningful latent features:
individual skill mastery, ability profile, and problem difficulty. The
motivation behind IKT is to address the shortcomings of existing knowledge
tracing models, such as Bayesian Knowledge Tracing (BKT), which struggle with
capturing learning transfer across different skills. Nkambou et al. [2023]
developed an advanced learner model within an Intelligent Tutoring System
(ITS) aimed at enhancing logical reasoning skills. This learner model is a
composite of several components, and it is designed to provide a personalized
learning experience by adapting to the individual needs of each student.

##### Goals and needs

Recognizing a user’s goals is a fundamental aspect of user modeling, focusing
on understanding the specific objectives users intend to achieve within the
application they are utilizing. On the other hand, comprehending information
needs is centered around the essential requirement of obtaining pertinent
information while, for instance, navigating the web. Modeling users’ goals and
needs has been a practice applied in every period of the history of user
profiling research. Horvitz et al. [1998] presented an in-depth exploration of
Bayesian user modeling with the primary goal of inferring the goals and needs
of users by analyzing their backgrounds, actions, and queries, thereby
providing tailored assistance. The paper by Zhou and Conati [2003] discusses
the development and evaluation of a causal model designed to infer user
emotions during interactions with an educational game. The model utilizes
nodes and links to deduce user goals based on personality traits and
interaction patterns. Barua et al. [2014] focused on the development and
evaluation of a user model designed to assist individuals in setting and
achieving personal long-term health-related goals. Drawing from psychological
theories, the authors created a Goal Model representation and a user interface
to facilitate goal setting and self-reflection. Recently, a few studies
explored user needs as a dynamic feature. For instance, Ma et al. [2022a]
designed and implemented the NEST (Need Evolution Simulation Testbed)
framework to model the dynamic nature of user preferences and needs,
particularly during extraordinary events that cause significant shifts in user
behavior.

##### Personality traits

In the context of user modeling, personality (or individual) traits are
defined as stable and enduring internal characteristics that change only over
a long period of time, and can be considered static. These traits, which
include learning and cognitive styles, reflect people’s characteristic
patterns of thoughts, feelings, behaviors, and habits, implying consistency
and stability. These features define a user as an individual and are used to
create the user model. This allows for different users interacting with the
same system to have unique experiences based on their individual traits. The
most widely used system of traits in psychology is the Five-Factor Model
[McCrae and John, 1992], and even though many works in user modeling have
referred to them (e.g., [Barnett et al., 2015]), contributions like that
presented by Cena et al. [2022] studied the impact of different specific
traits (i.e. need for recognition, locus of control, mindset and self-
efficacy) on the development of user models. In general, predicting users’
personality traits has seen applications in various contexts. Gao et al.
[2013] presented a method for deducing personality traits from social media
content, specifically targeting the Chinese language environment. By employing
machine learning techniques, the researchers developed a model capable of
predicting personality traits with a reasonable degree of accuracy. The
findings indicate that there is a significant correlation between the content
posted on social media and the personality dimensions of the users. Kim et al.
[2013] explored the impact of personality traits on the effectiveness of
e-learning systems. It presents the idea that understanding and integrating
personality traits into the design of e-learning platforms can lead to more
personalized and efficient learning experiences. Gou et al. [2014] examined
the idea of deriving personality traits from social media activity,
specifically Twitter, and analyzed user attitudes towards sharing these traits
in professional settings. The study aimed to determine the feasibility of
automatically extracting personality traits from social media and to
understand the factors influencing users’ willingness to share these traits at
work. Berkovsky et al. [2019] introduced a framework aimed at user modeling by
detecting personality traits through physiological responses to external
stimuli. The study specifically explores the use of affective image and video
stimuli alongside eye-tracking data to model user personality. Shen et al.
[2021a] presented a user profiling system designed to infer gender and
personality traits from non-linguistic audio data during conversations. The
system leverages conversational features such as turn-taking and interruption
patterns to identify user attributes effectively.

####  4.1.2 Dynamic content

Dynamic content refers to information that frequently and automatically
changes in response to user actions or system updates. This type of content is
generated in real-time or on a regular basis, often driven by algorithms that
analyze user behavior, preferences, and interactions with the system [Farid et
al., 2018]. A dynamic profile is autogenerated by the system, leading to
changes in user attributes and contents over time [Eke et al., 2019]. Despite
the recent surge of studies also considering the dynamicity of static
attributes, as mentioned in the previous section, common dynamic features in
user profiles are: behaviors, interests, and preferences.

##### Behaviors

User behaviors refer to the actions that users take while interacting with a
platform. This can include things like the pages they visit, the links they
click on, the amount of time they spend on certain tasks, and the frequency of
their visits [Farid et al., 2018; Eke et al., 2019]. By analyzing these
behaviors, platforms can gain insights into what users are interested in and
how they use the services [Kanoje et al., 2015]. Frequent actions on websites
or social networks are exploited to infer a user’s intentions (e.g., [Zhong et
al., 2012; Jiamthapthaksin and Aung, 2017]). Traditional methods rely on user
behavior and labeled data for modeling users, such as Covington et al. [2016],
who proposed a YouTubeNet model for video recommendation to model users by
leveraging their watched videos and search tokens. Other similar examples can
be found in Zhou et al. [2018] and Ouyang et al. [2019]. These techniques may
not be optimal when labeled data are scarce. To address this limitation, Wu et
al. [2020b] introduced a pre-trained user model (PTUM) approach, inspired by
the success of pre-trained language models in natural language processing
tasks, to capture the relatedness between past and future behaviors. However,
for modern systems in general, the domains where user behaviors are most
studied are e-commerce (e.g., [Gu et al., 2020, 2021c; Fan et al., 2022; Ma et
al., 2022b]) and general recommender systems (e.g., [Jin et al., 2020; Shen et
al., 2021b; Zhao et al., 2021; Qian et al., 2022; Xuan et al., 2023]).

##### Preferences and interests

Preferences and interests of users are fundamental aspects of personalization
[Farid et al., 2018]. They are related concepts, but they refer to different
aspects of an individual’s inclinations or likes. Preference is more about
choosing or favoring one option over another, often based on user personal
tastes or judgments (e.g., [Jiamthapthaksin and Aung, 2017; Cami et al., 2019;
Kostric et al., 2021; Zhang and Challis, 2021; Yang et al., 2022; Zheng et
al., 2022a]). To provide some specific examples, Kellner and Berthold [2012]
developed a tool to assess individual perceptual preferences in the context of
information processing, knowledge acquisition, and learning. The primary goal
of their contribution is to facilitate user profiling by identifying cognitive
preferences, which can then be used to tailor information presentation to
enhance user experience. Guo et al. [2018b] presented a personalized product
search model that leverages both visual and textual information to capture
user preferences. Majumder et al. [2019] introduced a method for personalized
recipe generation, which aims to assist users who have incomplete knowledge
about ingredients for specific dishes. The core of this study is the adoption
of historical user preferences to create tailored recipes based on partial
input specifications. Curmei et al. [2022] proposed a methodology that
integrates psychological principles into the development of dynamic user
preference models. The focus is on capturing the nuances of user behavior by
formalizing three classic psychological effects.

On the other hand, interest is about the level of curiosity or engagement a
user has in a particular subject or activity, which may or may not translate
into making choices or decisions (examples of general approaches employing
user interests, see Piao and Breslin [2018], Zhou et al. [2018], and Wang et
al. [2021a]). Wang et al. [2022b] presented an approach for enhancing user
profiling in the context of personalized point-of-interest recommendations by
leveraging a knowledge graph with temporal information to capture the dynamic
and evolving preferences of users with respect to various locations. Xuan et
al. [2023] introduced a framework designed to enhance user modeling in
recommender systems by leveraging multi-behavior information. This approach
acknowledges that different behaviors can reflect varying levels of user
preference and intent, providing a richer and more nuanced understanding of
user interests.

Dynamic profile that considers time may distinguish between short-term and
long-term interests. In user modeling research, short-term interests refer to
immediate preferences, subject to quick changes. In contrast, long-term
interests are enduring preferences that remain relatively stable over an
extended period, offering insights into consistent user likes and engagement
areas. Early approaches are dated to mid-2000s (e.g., [Díaz and Gervás, 2004;
Li et al., 2007; Liu et al., 2007]), but numerous significant contributions
have been published in recent years (e.g., [An et al., 2019; Guo et al., 2019;
Wu et al., 2019b; Yu et al., 2019; Xu et al., 2022; Liu et al., 2023b]). Hu et
al. [2020] implemented a graph-based model designed to capture both long-term
and short-term user interests to enhance the accuracy of news recommendations.
The model achieves this by constructing a heterogeneous graph that interlinks
users, news articles, and topics, thereby encapsulating the complex
relationships within the data. Sun et al. [2020] introduced an approach for
the next point of interest (POI) recommendation by incorporating both long-
term and short-term user preferences, as well as considering the spatial and
temporal contexts of user visits. Fazelnia et al. [2022] proposed a user
modeling model designed for music recommender systems that is distinctive in
its ability to capture both stable, long-term user interests and dynamic,
short-term preferences. The model differentiates between “slow features”,
which represent historical interactions indicative of enduring interests, and
“fast features”, which capture recent interactions reflecting immediate
preferences.

Take-home messages from user profile content.  • Combination of static and
dynamic content: User profiles blend static content (like demographics and
skills, which are stable) with dynamic content (such as behaviors and
preferences, which evolve over time) to create a full picture of the user. •
Detailed user data for personalization: In-depth static data covering a user’s
background, skills, and goals are key for personalized experiences, especially
in educational and adaptive systems. • Adaptive profiles with dynamic content:
Dynamic content, reflecting real-time user behaviors and preferences, allows
systems to adapt and personalize experiences, catering to both immediate and
long-term user interests.

###  4.2 Building and modeling user profiles

The process of building and modeling a user profile, which aims to capture,
record, and manage user needs, interests, and any kind of information
described in Section 4.1, incorporates several elements, from profile
construction and data collection techniques to their evaluation. In this
section, we discuss every component of the process.

####  4.2.1 User profile construction options

A crucial facet of user modeling revolves around profile construction, which
can be classified into individual and group profile modeling (or simply
individual/group profiling). Individual profiling relies on information linked
to a single user, such as enclosing aspects like demographic details, and it
refers to the standard and almost the entirety of the studies published in the
research area; On the other hand, group profiling involves a collective of
users who share common interests, goals, or preferences. In group profiling, a
partially complete profile is generated by overlapping information across
users.

##### Group profile modeling

Masthoff [2004] investigated the complexities of group modeling in the context
of adaptive television viewing, where the goal is to accommodate the
preferences of multiple viewers simultaneously. The research delves into how
individuals within a group make collective decisions about what to watch and
how satisfied they are with those decisions. Experiments conducted as part of
the study reveal that individuals consider factors like fairness, avoiding
individual misery, and overall group approval when making selections.
Generally, the contributions on group modeling are strictly related to the
topic of group recommender systems. Masthoff [2011] provided an in-depth
analysis of group recommender systems, focusing on the methodologies for
aggregating individual preferences to cater to a collective audience. The
article emphasizes the importance of understanding and modeling the affective
states of users to enhance the recommendation process for groups. Boratto and
Carta [2014] focused on the evaluation of various group modeling strategies
within group recommenders. The paper underlines the importance of accurately
combining individual preferences to generate group recommendations and
highlights the influence of group size and the diversity of individual
preferences on recommendation accuracy. The same authors implemented the
Automatic Recommendation Technologies (ART) framework [Boratto and Carta,
2015], which employs clustering algorithms to automatically detect groups of
users based on their preferences. The study identifies the Additive
Utilitarian strategy as the best approach for modeling group preferences. More
lately, Logesh et al. [2019] introduced an intelligent travel recommender
system designed to generate personalized recommendations for both individual
and group users. Central to this system is a user profiling module that
leverages a broad set of user-specific data. This data includes social,
contextual, behavioral, geographical, and categorical information. Zhou et al.
[2021a] presented a group-based personalized search model that leverages both
search behavior and friend networks to enhance search results for users,
particularly those with sparse historical data. Group profiles are created by
identifying friend circles through the user’s social connections and shared
search behaviors.

####  4.2.2 Data collection approaches

A central phase of user modeling involves collecting data about the user,
which can be obtained either through the user’s input or automatically
collected by an intelligent agent. This information gathering about a specific
user serves, first and foremost, as the starting point for any user modeling
technique. We categorize the data collection approaches for user profiling
into: explicit, pseudo-explicit, implicit, and hybrid.

##### Explicit user profiling

Explicit profiling approaches, also known as static or factual profiling, rely
on manual techniques that require user intervention through the completion of
online forms, the fulfillment of questionnaires, or the explicit provision of
ratings and preferences. These techniques were originally adopted by the
earlier contributions (examples are described in Raghu et al. [2001], Poo et
al. [2003], Brusilovski et al. [2007] and Schiaffino and Amandi [2009]). To
offer specific instances, Kern et al. [2008] investigated user attitudes
towards explicit profile generation for targeted advertising on public
electronic displays. The study reveals a preference among users for explicit
systems that grant them the ability to view and edit their profiles, despite
the additional effort required. It also indicates that users are inclined to
accept a less convenient system if it offers them greater control over their
personal data. Pannu et al. [2011] addressed the problem of low-quality web
search results by proposing a system that enhances search outcomes through
explicit profiling. The system employs the Vector Space Model (VSM) to
construct user profiles that reflect individual preferences and interests.
These profiles are then used to filter web documents by assessing the
similarity between the user profile and the document content.

Another side of this area considers research relying only on analyzing user’s
predictable and static characteristics. As illustrated in Section 4.1.1,
static content includes users’ demographic information, background, knowledge,
skills, goals, needs, and personality traits. Generally, a static profiling
contribution analyzes and studies different static features at the same time
(e.g., [Gao et al., 2013; Kim et al., 2013; Gou et al., 2014]). Zhou and
Conati [2003] developed a model for recognizing student emotions in real-time,
which employs the OCC cognitive theory of emotions (named after its
developers, Ortony, Clore, and Collins [Ortony et al., 1988]) and is
structured using Dynamic Decision Networks. The study conducted to refine the
model looked at student interaction patterns and goals, finding correlations
between personality traits and goals. More recently, Dong et al. [2017]
proposed a method called CoupledMFG (Multiple Label Factor Graph Model) for
demographic prediction in coupled networks, which are characterized by users
who have limited historical data within the network (i.e., a cold start
problem). This model captures the correlations between users’ communication
behaviors and demographic profiles, as well as the interrelations among
different demographic attributes. It utilizes both nonstructural attribute
features and structural features to infer demographic information. Fernandez-
Lanvin et al. [2018] provided a comprehensive examination of user attitudes
and behaviors in the realm of e-commerce, delving into various facets such as
user adaptation, success factors for online entrepreneurship, personality
modeling, and demographic-specific interaction strategies. A key focus of the
study is the impact of age and gender on web interaction performance. The
findings revealed that both age and gender play a significant role in
influencing user performance on e-commerce sites. Notably, the study observed
consistent performance across different interaction tasks among individuals,
suggesting the feasibility of developing a system that could automatically
classify users based on their interaction behaviors. Guo et al. [2018a]
presented an in-depth discussion on the evolution of user models and
introduced the Cyber-I model, an innovative approach aimed at creating a
comprehensive digital representation of an individual. The Cyber-I model is
designed to encapsulate the personality traits, characteristics, and behaviors
of users in the digital realm, striving to closely mirror the real individual.
Rozen et al. [2021] introduced ProfBERT, a model that leverages a BERT-based
framework combined with attention networks to generate user representations
from comments on news articles. This model utilizes both user-generated
comments and the summaries of the articles they comment on to create detailed
user profiles. The attributes predicted include gender, location type, and
mobile device usage.

##### Pseudo-explicit user profiling

The practice of utilizing static data for user profiling has evolved in recent
years. This information is extracted from public data that users have
willingly shared for various reasons, such as setting up social networking
accounts or using travel platforms. We propose the term pseudo-explicit user
profiling for this newly emerged category. To elaborate, pseudo-explicit user
profiling refers to a method where explicit user information is gathered not
directly from the user for the purpose of profiling, but indirectly from data
that the user has shared publicly for other purposes. This could include data
shared on social media platforms, travel booking sites, online forums, and
more. This method offers a new approach to user profiling, leveraging the
wealth of data users generate as they interact with various platforms on the
internet. It is called “pseudo-explicit” because while the information is
explicit and voluntarily provided by the users, it was not shared with the
intention of being used for profiling. The papers by Shu et al. [2018, 2019]
focused on the role of user profiles in the context of fake news detection on
social media, by exploiting explicit information extracted directly from
metadata returned by querying social network APIs. Zhang et al. [2020]
demonstrated that the daily EV charging load profiles vary with different
demographic and social attributes by presenting a refined EV charging load
simulation method considering people’s demographics and social characteristics
(e.g., gender, age, education level) retrieved from the US National Household
Travel Survey.

##### Implicit user profiling

Exclusively depending on explicit profiling became a challenge as users
hesitated to share their information due to privacy concerns or found the
process of filling out forms to be unmanageable, leading to avoidance. Modern
systems have changed their perspective by giving more importance to implicit
user profiling, also known as behavioral, dynamic, or adaptive profiling. This
involves the passive collection and analysis of dynamic user data, including
observing user behavior, interactions, and preferences, without necessitating
direct input from the user (e.g., [Kasper et al., 2017; Pujahari and Sisodia,
2022; Qi et al., 2022a; Li et al., 2023b]). Implicit profiling approaches
involve several aspects, including behavior, preference, interest, and
intention modeling.

Behavior modeling is about observing and analyzing users’ actions,
interactions, and patterns while they engage with a system or application.
This could include tracking the pages they visit, the time spent on different
tasks, and the sequences of actions performed (e.g., [Zhong et al., 2012;
Covington et al., 2016; Zhou et al., 2018; Wu et al., 2020b; Morshed Fahid et
al., 2021; Zhao et al., 2021; Agarwal et al., 2022; Fan et al., 2022; Qian et
al., 2022; He et al., 2023]). Wang et al. [2020a] presented a deep learning
architecture to predict user attributes by learning from spatiotemporal
behavior. Gu et al. [2021c] introduced a framework designed to enhance ranking
models in e-commerce by leveraging user behaviors and utilizing a two-stage
approach: pre-training on users’ spontaneous behaviors and fine-tuning on
implicit feedback across multiple e-commerce scenarios. Zhu et al. [2021]
designed a system to enhance document ranking by optimizing the representation
of user behavior sequences through contrastive learning. The system focuses on
capturing the nuances of user behavior sequences to improve document ranking.
Several methods introduced approaches for constructing user profiles which
rely on the implicit collection of user browsing data to generate precise
profiles, whose accuracy can be improved by integrating various sources of
browsing data and by differentiating between significant and insignificant
concepts (e.g., [Kulkarni et al., 2019; El-Ansari et al., 2020; Gu et al.,
2021c; Bedi et al., 2022; Han et al., 2022; Yan et al., 2022]). Different
contributions exploited the user’s dialogue history from conversational
platforms to build the profiles (e.g., [Ma et al., 2021; Qian et al., 2021; Li
et al., 2022c]).

The analysis of behavior profiling has advanced considerably in the last few
years, incorporating a range of refined modeling techniques that offer a more
profound insight into user behavior across diverse contexts:

  * •

Micro and macro behavior modeling refers to the study of different levels of
user interactions and activities within an online platform, particularly in
the context of e-commerce and recommender systems, reflecting, respectively,
short-term and long-term user preferences or interests. Gu et al. [2020]
developed a deep learning framework to capture user actions and dynamically
model their interests at different temporal levels. Wen et al. [2021]
introduced a deep neural recommendation model designed to address two critical
challenges in the field: sample selection bias and data sparsity. They
achieved this by incorporating both micro and macro user behaviors into a
unified framework, which allows for a more comprehensive understanding of user
interactions.

  * •

Multi-behavior modeling integrates diverse user interactions with items,
moving beyond dependence on a single type of interaction (e.g., [Jin et al.,
2020; Xia et al., 2021a, b; Zhang et al., 2022; Cheng et al., 2023]). To cite
specific recent cases, Cho et al. [2023] introduced two user modeling
approaches, DyMuS and DyMuS+, designed to address the inherent challenges in
multi-behavior data, such as data imbalance, heterogeneity, and the need for
personalized recommendations. Xuan et al. [2023] proposed the Knowledge
Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR)
framework, aiming to augment user modeling in recommender systems through the
utilization of multi-behavior information. This strategy recognizes that
various behaviors can indicate different degrees of user preference and
intent, thereby offering a more comprehensive and nuanced comprehension of
user interests.

  * •

Sequential behavior modeling considers the order and timing of user actions,
acknowledging that the sequence of behaviors can impact a user’s interests.
This temporal aspect holds significance in many application domains, as the
sequence of user interactions can unveil evolving preferences and contribute
to predicting future actions (e.g., [Ren et al., 2019; Yuan et al., 2020a; Cao
et al., 2022]). To provide explicit samples, Bian et al. [2021] presented a
Contrastive Curriculum Learning (CCL) framework designed to enhance the
modeling of sequential user behaviors and produce more effective user behavior
representations. Chen et al. [2022a] introduced the Auto-Session-Encoder
(ASE), a novel model designed to improve the modeling of user behavior
sequences in session search. The conducted experimental studies revealed that
the proposed model benefits more from predicting future sequences and clicked
documents rather than recovering historical ones.

  * •

Hierarchical user profiling is relevant to personalized e-commerce
recommendations and models real-time interests at varying levels. Strictly
related to the concepts of micro and macro behavior modeling, this yields
nuanced insights for accurate predictions, enhancing precision and tailoring
user experiences in the dynamic e-commerce landscape. Gu et al. [2020]
engineered a Hierarchical User Profiling (HUP) framework to capture and model
users’ real-time interests at varying levels of granularity, acknowledging the
hierarchical structure inherent in product categories and user interactions.
Wen et al. [2021] introduced a deep neural recommendation model, designed to
enhance conversion rate prediction by hierarchically modeling both micro and
macro behaviors. By considering both levels of behavior, the model can better
understand and anticipate user actions, leading to more effective
recommendations and higher conversion rates. In the same research territory,
Li et al. [2022a] designed a framework for enhancing cross-domain click-
through rate prediction by incorporating a hierarchical user behavior modeling
approach that leverages an element-wise behavior transfer layer and a user
representation layer. Wei et al. [2022] and Xue et al. [2022] employed Graph
Neural Networks to hierarchically analyze multi-level user intents and item
representations to improve the accuracy of recommendations.

  * •

Mobile user profiling identifies user interests and behavior patterns from
their mobile device activities. With smartphones being widespread and
generating substantial data, this profiling is vital for delivering
personalized content and services that suit users’ dynamic lifestyles. Bhogi
et al. [2019] presented an approach to user profiling for mobile phone users,
particularly focusing on the challenge of creating accurate profiles without
access to ground truth data. The core of the proposed approach is the
identification of seed features that are indicative of certain user
attributes. These features are derived from user behavior and are used to
train a positive unlabeled learning model. Wang et al. [2019b, 2020b, 2021b]
are particularly active in this area. They proposed several innovative deep
learning approaches for studying the unique characteristics and preferences of
mobile users. Zhao et al. [2022] introduced a graph-based model for improving
mobile user profiling by leveraging app text data. Traditional methods
struggle with the sparse semantics and limited context of this kind of data,
but the proposed model addresses these issues by constructing two
heterogeneous graphs and applying a selective-scale attention mechanism to
better capture semantic information.

Preference modeling involves deducing user preferences based on their past
interactions and choices. By analyzing the historical data of user
preferences, systems can make predictions about what a user might prefer in
the future. This is one of the historical areas exploited in user modeling
research (e.g., [Amatriain et al., 2009; Lakiotaki et al., 2011; Fu et al.,
2013]), and studies are currently applied in various contexts, such as
recommendations, personalized user interfaces, or targeted advertising. To
illustrate with particular examples, Zhao et al. [2019a] focused on modeling
user-system interactions within recommender systems by implementing a two-
stage framework and a specific neural model. The model is specifically
designed to better capture user behavior and preferences by considering the
temporal dynamics of user interactions. Wu et al. [2020a] developed a model to
capture a more holistic view of user preferences by not only considering which
articles are clicked but also how users interact with them in terms of reading
time. The experimental results demonstrated that incorporating reading
satisfaction into user modeling significantly enhances the quality of news
recommendations. Kostric et al. [2021] presented a method for improving
preference elicitation in conversational recommender systems. The core idea is
to generate implicit questions that can be used to understand user preferences
more effectively. Gomez Bruballa et al. [2022] introduced a recommender system
specifically designed for an image marketplace, with the primary goal of
learning and catering to users’ preferred visual styles. Yang et al. [2022]
proposed a user preference modeling approach in the context of online
recruitment, specifically addressing the challenge of person-job fit.

Interest modeling focuses instead on identifying and understanding the topics,
subjects, or content that capture a user’s attention. This is often inferred
from the content they engage with, the keywords they search for, or the types
of products they explore. By building a model of user interests, systems can
provide more relevant and tailored recommendations, content suggestions, or
targeted information. Cami et al. [2019] introduced a Bayesian non-parametric
approach to capture and adapt to the evolving preferences and interests of
users. Yilma et al. [2021] developed a recommendation and guidance method that
is sensitive to the interests and needs of multiple stakeholders, including
visitors, curators, and those responsible for crowd management. The novelty of
the proposed method lies in its ability to balance user interests with other
factors, such as the popularity of Points of Interest (POIs) and the
objectives set by the curators.

Other relevant contributions for preference and interest modeling can be found
in Jiamthapthaksin and Aung [2017], Logesh et al. [2019], Majumder et al.
[2019], Olaleke et al. [2021], Zhang and Challis [2021], Curmei et al. [2022],
Fan et al. [2022], and Zheng et al. [2022a].

Noteworthy to mention is the simultaneous focused research on both short- and
long-term preference (or interest) modeling (e.g., [An et al., 2019; Hu et
al., 2020; Sun et al., 2020; Zhou et al., 2020a; Fazelnia et al., 2022; Liu et
al., 2023b]). Guo et al. [2019] introduced a neural network model named ALSTP
(Attentive Long- and Short-Term Preference) designed for personalized product
search. The model aims to enhance search accuracy by incorporating both the
long-term and short-term preferences of users, along with the current search
query. Wu et al. [2019b] developed a model to recommend the next point of
interest (POI) to users. The model is designed to incorporate both the long-
term preferences and the short-term behaviors of users, as well as contextual
factors like POI categories and check-in times. Yu et al. [2019] implemented a
recommender that adeptly captures both short-term and long-term user
preferences. The approach is tailored to address the dynamic nature of user
behavior, which is characterized by changing time intervals and latent
intents. Xu et al. [2022] presented a recommendation engine designed by
Pinterest to address the dual temporal aspects of user behavior: long-term
interests and short-term intentions. The engine incorporates user embeddings
that are specifically optimized to predict long-term future actions, as well
as sequences of real-time actions to capture immediate user intent.

Intention modeling covers an important part of the behavior modeling research.
It involves predicting or understanding the user’s goals, objectives, or
intentions based on their behavior and interactions. By analyzing the sequence
of actions and choices made by a user, systems can infer what the user is
likely to pursue or achieve. This aspect is valuable in providing anticipatory
and proactive support, aligning system responses with the user’s intended
outcomes. Intention modeling contributes to a more dynamic and responsive user
experience, as the system can adapt to user goals in real-time, offering
relevant assistance or suggestions. Lugo et al. [2021] presented an approach
to user search task modeling, whose key aspect is its focus on user intent
modeling, which is achieved by analyzing clicked URLs from a large-scale
query-clicked document collection. Deng et al. [2022a] introduced a
personalized search model that leverages a dual-feedback network to enhance
the understanding of user search intentions. The model is designed to improve
personalized search by incorporating multi-granular user feedback, both
positive and negative, to accurately model the user’s current search
intention. Li et al. [2023a] proposed an approach that defines user intent as
a probability distribution of item categories and behaviors. This allows the
system to understand the user’s multiple objectives and preferences more
accurately. The model utilizes user intents as guidance for fusing user
preferences across different behavior objectives, rather than learning user
preference for each intent separately.

##### Hybrid user profiling

This user profiling approach, which combines both implicit and explicit user
profiling methods, integrates the benefits of both strategies. It considers
static characteristics and retrieves behavioral information about a user,
enhancing profiling efficiency. Examples can be found in Poo et al. [2003],
Stanescu et al. [2013], Luo et al. [2014], and Liu [2015]. To supply detailed
instances, Du et al. [2016] addressed the challenge of personalized resource
search within collaborative tagging systems by introducing a novel hybrid user
profiling model. The model is designed to enhance the personalization of
search results by incorporating both tags and ratings, thereby capturing a
user’s preferences and aversions more accurately. In Natarajan and Moh [2016],
the hybrid user profiles are constructed using a combination of click-through
analysis, user tweet analysis, and user follower analysis. This multifaceted
approach to profile building allows the system to capture a more comprehensive
view of user preferences. Lately, Logesh et al. [2019] and Nkambou et al.
[2023] provided hybrid approaches leveraging data encompassing contextual,
behavioral, geographical, and categorical information, which is instrumental
in understanding the user’s preferences and activities, particularly in online
social networks and recommender systems.

####  4.2.3 Data preprocessing

A significant portion of profile data obtained, especially dynamic content
from social media and recommendation engines, is often incorrect.
Consequently, there is a necessity to cleanse the acquired datasets to ensure
that the extracted features for profile modeling yield improved performance
results. Many researchers adopt diverse data preprocessing techniques in their
studies on user profiling to ready their data for the subsequent analysis
phase. For instance, Tang et al. [2010] employed a Hidden Markov Model
approach to efficiently purify data, improving the user profile system. This
method successfully addressed the issue of disambiguation in collecting user
profile information. Basic pre-processing techniques, including tokenization,
stop word removal, and tagging, were applied. Tokenization involves segmenting
textual data into tokens using a tokenizer, with each token being assigned a
tag. Recently, Ali et al. [2020] provided a thorough examination of the data
preprocessing steps necessary for effective web usage mining, which is the
process of extracting useful information from web usage data to model user
behavior. The paper outlines the various sources of web data, such as click-
stream analysis and web server log files, and emphasizes the importance of
preprocessing in the overall behavior user modeling process.

####  4.2.4 Feature extraction

Feature extraction is another important step in user modeling, involving the
retrieval of user profile features from diverse domains. Various procedures
have been employed by researchers in this field to extract these features,
aiming to enhance modeling performance. Commonly used elements include content
features, pattern features, profile features, term features, and user
behavioral features. For instance, Hijikata [2004] proposed a method for text
data extraction based on user mouse behavior features on web platforms. Tang
et al. [2010] focused on authorship profiling, evaluating a set of profile
features consisting of six sets of attributes in article publication data,
such as publication title, abstract, venue, abstract authors, publication
year, and references. These features were extracted from digital libraries
(ACM, Springer, and IEEE) using heuristics and represented as a feature vector
with the number of occurrences as values. Meftah et al. [2012] introduced a
method that focuses on the extraction of relevant features from physiological
data, such as electromyography (EMG) and respiration signals, which are
critical for accurately identifying not only basic but also complex emotions.
The feature extraction process is a part of the training module designed to
discern patterns within the physiological signals that correspond to different
emotional states. Li et al. [2014] utilized a supervised learning classifier
to extract data from Twitter and train the model. However, this approach
solely took into account network information, which proves insufficient for
inferring user attributes. Ben Hassen et al. [2022] proposed a method to
extract latent features from images of items, such as movie posters and
clothing, by leveraging pre-trained deep learning models. The extracted
features are then subjected to dimensionality reduction to make them more
manageable and suitable for the subsequent stages of the recommendation
process.

####  4.2.5 User profile representation

User profile representation refers to the way in which information stored in
user profiles is structured and presented through a system or an application.
The goal is to create an accurate portrayal of the user, enabling the system
to make informed decisions and predictions about the user’s interactions and
preferences. We discuss several representation variants: term-based, semantic-
network, concept-based, graph-based, universal, and holistic.

##### Term-based (Vector-space model)

The prevalent representation of users’ interests is through a keyword-based or
vector-based user model, comprising a set of terms weighted by vectors of
keywords. These vectors, utilized to expand user queries, can be developed
using different methods like Boolean, Term Frequency (TF), or Term Frequency-
Inverse Document Frequency (TF-IDF) [Shen et al., 2005]. User interest
keywords, extracted from visited documents during browsing, can be represented
by a single vector encompassing all interests or multiple vectors reflecting
interests in various domains. The effectiveness of this model relies on the
degree of generalization in the vectors [Gauch et al., 2007]. However,
drawbacks include polysemy and potential misinterpretation of user interests.
An interesting article was presented by Hu et al. [2017], who introduced an
extraction algorithm that combines Word2Vec and TF-IDF to address shortcomings
in the vector representation method.

##### Semantic-based (Ontology-based)

To tackle the challenges of polysemy and synonymy in keyword-based profiles, a
solution involves representing profiles using weighted concepts in a semantic
network. However, this introduces complexities in system construction,
requiring an existing mapping between words of interest and concepts, such as
WordNet ontology, a learning system method, or manual intervention. Sieg et
al. [2007] explored the concept of personalizing web search results through
the use of ontological user profiles. These profiles are built by assigning
interest scores to concepts within a domain ontology. Skillen et al. [2012]
introduced an ontological user profile model designed to enhance adaptive,
context-aware applications in mobile environments. The model emphasizes the
importance of dynamic user attributes and contains classes that represent user
preferences, education, health profiles, capabilities, interests, and
contextual information. The goal of the model is to facilitate personalized
services that can adapt to users as they transition through different
environments. In the context of recommender systems, Rimitha et al. [2018]
proposed an ontology that encapsulates various aspects of the job
recommendation domain, such as location, education, salary ranges, job
description, and job experience. The ontology is designed to provide a
structured and standardized representation of user preferences and job
characteristics, which includes defining classes and the relationships between
them. For instance, it includes relations like “Has preferred salary” and “Has
preferred location”, which are crucial for matching users with suitable job
opportunities. In the same domain, Olufisayo Dahunsi [2021] presented a
proposal for an advanced user profiling methodology within the context of
apparel recommender systems. The proposed methodology is centered around the
development of an ontology-based knowledge base designed to enhance the
personalization of apparel recommendations by mapping user features to garment
and ensemble features, utilizing domain expert knowledge and predefined style
rules.

##### Concept-based (Hierarchy-based)

Concept-based profiles and semantic network-based profiling share the common
approach of representing information through nodes of concepts and the
relationships between them. Different methods are employed to assign weights
indicating the user’s level of interest in each topic. Nanas et al. [2003]
utilized various techniques to categorize user interests within a hierarchical
structure, employing a knowledge base for this purpose. The researchers
applied a library classification system (LCS) to identify user interest
hierarchies, while the edge weights were calculated using the TF-IDF
mechanism. Malinowski and Zimányi [2006] introduced a conceptual framework
designed to enhance data warehouses and Online Analytical Processing (OLAP)
systems. The model builds upon the Entity-Relationship (ER) model,
incorporating specific constructs to represent dimensions, hierarchies, and
fact relationships, and provides a graphical notation for these elements.
Recently, Wu et al. [2019a] developed a hierarchical user and item
representation model that leverages a three-tier attention mechanism to
enhance recommender systems. This model is designed to extract and learn
representations from user and item reviews at multiple levels of granularity,
including words, sentences, and reviews themselves.

##### Graph- and Knowledge graph-based

Graph-based representation in user modeling involves utilizing graphs or
networks to represent and analyze information about users. This approach is
particularly effective in capturing complex relationships, dependencies, and
interactions among various elements in a user’s profile. The representation is
typically structured with nodes representing entities or concepts and edges
denoting relationships or connections between them. In the last few years,
works leveraging graph-structures increased. Wang et al. [2019b] introduced an
adversarial substructured learning framework tailored for mobile user
profiling. The approach hinges on the construction of user activity graphs
that encapsulate the unique characteristics and preferences of mobile users.
The central challenge addressed is the learning of deep representations from
these graphs to accurately profile users Chen et al. [2022b] presented a novel
approach named Global and Personalized Graphs for Heterogeneous Sequential
Recommendation (GPG4HSR) that enhances user profiling and modeling in the
context of heterogeneous sequential recommender systems. The proposed model
incorporates two distinct graph layers to improve user profiling. The first is
a global graph layer that captures the transitions between different behaviors
across all users, providing a macro-level view of user interactions. The
second is a personalized graph layer that models the interaction sequences of
individual users, taking into account the unique intentions and preferences of
each user by focusing on the contextually relevant nodes adjacent to their
interactions. Guan et al. [2022] implemented a novel approach designed to
enhance the personalization of fashion compatibility recommendations by
leveraging user preference modeling. The proposed model employs a
heterogeneous graph that integrates users, items, and attribute entities,
along with their interrelations, to create a comprehensive representation of
the fashion domain. Other relevant contributions in the area can be seen in
Guo et al. [2018a], Wanda and Jie [2020], Wang et al. [2020b], Yang et al.
[2022], and Yang et al. [2023].

Knowledge graphs are essential in user modeling, providing a robust framework
to organize and represent complex relationships. In user modeling, they
efficiently capture users’ preferences and behaviors, enabling personalized
experiences and enhancing recommender systems. By mapping connections between
users and their interests, knowledge graphs facilitate a nuanced understanding
of individual profiles, allowing systems to adapt dynamically to users’ unique
needs and preferences. Wang et al. [2020b] presented an innovative approach to
mobile user profiling by proposing an integrated reinforcement learning
framework that incorporates spatial knowledge graphs. Anelli et al. [2021]
introduced a knowledge-aware recommender system that emphasizes user modeling
through a sparse factorization approach. By analyzing historical data and item
attributes, the model constructs a detailed model of user-item interactions,
which is essential for understanding user preferences and tailoring
recommendations. Instances of similar works are seen in Huang et al. [2019],
Wang et al. [2019b], Wang et al. [2021a], and Xuan et al. [2023].

##### Universal user representation

It aims to create a generalized user representation that encapsulates the
essential characteristics and behaviors of a user. This representation is
constructed to be versatile and applicable across a variety of real-world
applications, without the need for specific adjustments for each task.
Universal user representation emphasizes adaptability and versatility to
accommodate a wide range of users and their diverse needs, seeking to create a
model that can be applied across different contexts and user groups. For
instance, Ni et al. [2018] developed a deep learning framework designed to
create universal user representations for a variety of e-commerce
personalization tasks. The proposed architecture is capable of simultaneously
performing multiple personalization tasks, such as click-through rate (CTR)
prediction, learning to rank (L2R), price preference prediction, fashion icon
following prediction, and shop preference prediction. The model is trained on
a substantial offline dataset from Taobao, one of the largest e-commerce
platforms. Yuan et al. [2020a] introduced a transfer learning architecture