# Personalized News Recommendation: Methods and Challenges

Chuhan Wu  Department of Electronic Engineering & BNRist, Tsinghua
UniversityBeijing100084China<EMAIL_ADDRESS>,  Fangzhao Wu  Microsoft
Research AsiaBeijingChina100080<EMAIL_ADDRESS>,  Yongfeng Huang
Department of Electronic Engineering & BNRist, Tsinghua
UniversityBeijing100084China<EMAIL_ADDRESS>and  Xing Xie  Microsoft
Research AsiaBeijingChina100080<EMAIL_ADDRESS>
(2021)

###### Abstract.

Personalized news recommendation is important for users to find interested
news information and alleviate information overload. Although it has been
extensively studied over decades and has achieved notable success in improving
user experience, there are still many problems and challenges that need to be
further studied. To help researchers master the advances in personalized news
recommendation, in this paper we present a comprehensive overview of
personalized news recommendation. Instead of following the conventional
taxonomy of news recommendation methods, in this paper we propose a novel
perspective to understand personalized news recommendation based on its core
problems and the associated techniques and challenges. We first review the
techniques for tackling each core problem in a personalized news recommender
system and the challenges they face. Next, we introduce the public datasets
and evaluation methods for personalized news recommendation. We then discuss
the key points on improving the responsibility of personalized news
recommender systems. Finally, we raise several research directions that are
worth investigating in the future. This paper can provide up-to-date and
comprehensive views on personalized news recommendation. We hope this paper
can facilitate research on personalized news recommendation as well as related
fields in natural language processing and data mining.

news recommendation, personalization, survey, user modeling, natural language
processing

††copyright: acmcopyright††journalyear: 2021††price: 15.00††ccs: Information
systems Recommender systems††ccs: Information systems Personalization††ccs:
Computing methodologies Natural language processing

##  1\. Introduction

In the era of the Internet, online news distributing platforms such as
Microsoft News111https://microsoftnews.msn.com have attracted hundreds of
millions of users (Wu et al., 2020a). Due to the convenience and timeliness of
online news services, many users have shifted their news reading habits from
conventional newspapers to digital news content (Okura et al., 2017). However,
a large number of news articles are created and published every day, and it is
impossible for users to browse through all available news to seek their
interested news information (Wu et al., 2019c). Thus, personalized news
recommendation techniques, which aim to select news according to users’
personal interest, are critical for news platforms to help users alleviate
their information overload of users and improve news reading experience (Li
and Wang, 2019). Researches on personalized news recommendation have also
attracted increasing attention from both academia and industry in recent years
(Okura et al., 2017; Wu et al., 2019b).

An example workflow of personalized news recommender system is shown in Fig.
1. When a user visits the news platform, the news platform will recall a small
set of candidate news from a large-scale news pool, and the personalized news
recommender will rank these candidate news articles according to the user
interests inferred from user profiles. Then, the top K ranked news will be
displayed to the user, and the user behaviors on these news will be recorded
by the platform to update the maintained user profile for providing future
services. Although many prior works have extensively studied these problems in
different aspects, personalized news recommendation remains challenging. For
example, news articles on news websites usually have short life cycles. Many
new articles emerge every day, and old ones will expire after a short period
of time. Thus, news recommendation faces a severe cold-start problem. In
addition, news articles usually contain rich textual information such as title
and body. Thus, it is very important to understand news content from their
texts with advanced natural language processing techniques. Moreover, there is
usually no explicit user feedback such as reviews and ratings on news
platforms. Thus, we need to infer the personal interests of users from their
implicit feedback like clicks. However, user interests are usually diverse and
dynamic, which poses great challenges to user modeling algorithms. The
complexity of personalized news recommendation makes it a fascinating research
topic with various challenges to be tackled (Feng et al., 2020).

Figure 1. An example workflow of personalized news recommender systems. Table
1. The taxonomy and literature coverage of recent survey papers. Traditional
taxonomy means the collaborative, content-based, and hybrid categories. DL
stands for deep learning.

Reference | Year | Taxonomy | Literature Coverage
---|---|---|---
(Özgöbek et al., 2014) | 2014 | Traditional taxonomy | Feature-based
(Sood and Kaur, 2014b) | 2014 | Traditional taxonomy | Feature-based
(Durairaj and Kumar, 2014) | 2014 |  | Collaborative, content-based, demographic-based,
---
utility-based, and knowledge-based
Feature-based
(Karimi et al., 2018) | 2018 | Traditional taxonomy | Feature-based
(Li and Wang, 2019) | 2019 | Traditional taxonomy | Feature-based
(Feng et al., 2020) | 2020 | Traditional taxonomy and application scenarios | Feature-based & a few DL-based
(Qin, 2020) | 2020 | Traditional taxonomy | Feature-based & a few DL-based
(Iana et al., 2021) | 2021 | Based on knowledge base structure | Knowledge-based methods
(Raza and Ding, 2021b) | 2021 | Traditional taxonomy | Feature-based & parts of DL-based
|  |  |

A comprehensive overview of existing personalized news recommendation
approaches can provide useful guidance for future research in this field. Over
the past years, there are many survey papers that review the techniques of
news recommendation (Bogers and Van den Bosch, 2007; Borges and Lorena, 2010;
Li et al., 2011c; Sood and Kaur, 2014b; Özgöbek et al., 2014; Durairaj and
Kumar, 2014; Doychev et al., 2014; Harandi and Gulla, 2015; Dwivedi and Arya,
2016; Karimi et al., 2018; Li and Wang, 2019; Feng et al., 2020; Qin, 2020).
For example, Li et al. (Li and Wang, 2019) reviewed the personalized news
recommendation methods based on handcrafted features to build news and user
representations. They covered many traditional feature-based methods,
including collaborative filtering (CF) based ones that use the IDs of users
and news, content-based ones that use features extracted from the content of
news and the user behaviors on news, and hybrid ones that rely on content-
based collaborative filtering. They also studied the datasets used by these
methods and their techniques for user and news representation construction,
data processing and user privacy protection. Feng et al. (Feng et al., 2020)
reviewed news recommendation approaches in many different scenarios including
personalized and non-personalized ones. For personalized news recommendation
methods, they also classify them into three categories, i.e., CF-based,
content-based, and hybrid. They mainly studied the techniques adopted by
different methods, the challenges they tackled, and the datasets and metrics
for evaluation. We summarize the taxonomy of news recommendation methods and
the literature coverage of several recent survey articles in Table 1. We find
that most surveys mainly focus on traditional feature-based methods and only a
small part of deep learning-based methods are covered by a few recent surveys,
which is not beneficial for researchers to track recent advances in the
personalized news recommendation field. In addition, most surveys follow the
canonical taxonomy that categorizes news recommendation methods based on
whether they rely on collaborative or content-based filtering techniques.
However, it is difficult for this taxonomy to distinguish between traditional
methods and recent deep learning-based approaches. In addition, the hybrid
category contains methods based on quite diverse techniques, e.g., traditional
CF-enhanced content matching and graph neural network, which is not beneficial
for researchers to be aware of the evolution of news recommendation
technologies. Moreover, several key techniques in news recommender system
design, such as ranking and model training, are rarely discussed in this
paradigm. Thus, the conventional taxonomy used by most existing surveys cannot
meet the development of this field, and a more systematic taxonomy of existing
news recommendation methods is needed to help understand their characteristics
and inspire further research.

In this paper, we present a comprehensive review of the personalized news
recommendation field. Instead of reviewing existing personalized news
recommendation methods based on the conventional taxonomy, in this survey we
propose a novel perspective to review them based on the core problems involved
in personalized news recommendation and the associated techniques and
challenges. We first introduce the framework of developing a personalized news
recommender system in Section 2. Next, we systematically review the core
problems, techniques and challenges in personalized news recommendation,
including: news modeling, user modeling, personalized ranking, model training,
datasets, benchmarks and evaluation, which are introduced in Sections 3-7,
respectively. Through our proposed framework, the characteristics of existing
approaches can be more accurately described than using conventional taxonomy,
and it is easier for researchers to track the technology evolution in
different aspects. We then present some discussions on developing responsible
news recommender systems in Section 9, which is an emerging research field in
recent years. Finally, we raise several potential future directions and
conclude this paper in Section 10.

##  2\. Framework of Personalized News Recommendation

Personalized news recommendation techniques have been widely used in many
online news websites (Okura et al., 2017; Wu et al., 2020a). Different from
non-personalized news recommendation methods that suggest news articles solely
based on non-personalized factors (Lavrenko et al., 2000) such as news
popularity (Corsini and Larson, 2016; Yang, 2016; Ludmann, 2017; Lommatzsch et
al., 2018), editors’ demonstration (Wang et al., 2017) and geographic
information (Son et al., 2013; Chen et al., 2017), personalized news
recommendation can consider the personal interest of each individual user to
provide personalized news services and better satisfy users’ need.

Figure 2. A framework of the key components in developing personalized news
recommendation model.

Existing surveys on personalized news recommendation usually classify methods
into three categories, i.e., collaborative filtering-based, content-based and
hybrid (Li and Wang, 2019). However, this taxonomy cannot adapt to the recent
advances in news recommendation because many methods with diverse
characteristics fall in the same category without distinguishment. For
example, the category of content-based methods includes traditional semantic-
based methods, contextual bandit-based methods and recent deep learning-based
methods, which is difficult to characterize the paradigm and technical
evolution of personalized news recommendation. Thus, a more systematic
overview of existing techniques is required to help understand the development
of this field.

Instead of following the conventional taxonomy, in this survey we propose a
novel perspective to review existing personalized news recommendation
techniques based on the core problems involved in the development of a
personalized news recommender system. A common framework of personalized news
recommendation model development is shown in Fig. 2. We can see that there are
several key problems in this framework. First, news modeling is the backbone
of news recommendation and a core problem is how to understand the content and
characteristics of news. In addition, user modeling is required to understand
the personal interest of users in news, and it is critical to accurately infer
user interest from user profiles like behaviors. Based on the news and user
representations built by the news and user models, the next step is ranking
candidate news according to certain policies such as the relevance between
news and user interest. Then, it is important to train the recommendation
model with proper objectives to make high-quality news recommendations, and
evaluating the ranking results given by the recommendation model is also a
core problem in the development of personalized recommender systems. Besides,
the datasets and benchmarks for news recommendation are also necessities in
designing personalized news recommendation models. Moreover, beyond developing
accurate models, improving the responsibility of intelligent systems has been
a spotlight problem in recent years. How to develop responsible news
recommender systems is a less studied but extremely important problem in
personalized news recommendation. Next, we briefly discuss the key problems
mentioned above in the following sections.

###  2.1. News Modeling

News modeling aims to understand the characteristics and content of news,
which is the backbone of news recommendation. There are mainly two kinds of
techniques for news modeling, i.e., feature-based news modeling and deep
learning-based news modeling. Feature-based news modeling methods usually rely
on handcrafted features to represent news articles. For instance, in many
methods based on collaborative filtering (CF), news articles are represented
by their IDs (Resnick et al., 1994; Das et al., 2007). However, on most news
websites novel news articles are published continuously and old ones soon
vanish. Thus, representing news articles with their IDs will suffer from
severe cold-start problems, and the performance is usually suboptimal.

Considering the drawbacks of ID-based news modeling methods, most approaches
incorporate content features to represent news. Among them, many methods use
features extracted from news texts for news modeling. For instance, Capelle et
al. (Capelle et al., 2012) proposed to represent news with Synset Frequency-
Inverse Document Frequency (SF-IDF), which uses WordNet synonym set to replace
the term frequencies in TF-IDF. Besides the news texts, many methods also
explore to incorporate various factors that may have influence on users’ news
browsing decisions into news modeling, such as news popularity and recency (Li
et al., 2011b). However, in these methods, the features to represent news are
usually manually designed, which usually requires much effort and domain
knowledge. In addition, handcrafted features are usually not optimal in
representing the semantic information encoded in news texts.

With the development of natural language processing techniques in recent
years, many methods employ neural NLP models to learn deep representations of
news. For example, Okura et al. (Okura et al., 2017) proposed to use
autoencoders to learn news representations from news content. Wang et al.
(Wang et al., 2018) proposed to use a knowledge-aware convolutional neural
network (CNN) to learn news representations from news titles and their
entities. Wu et al. (Wu et al., 2019e) proposed to learn news representations
from news titles via a combination of multi-head self-attention and additive
attention networks. Wu et al. (Wu et al., 2021c) studied to use pre-trained
language models to encode news texts. These deep learning-based news modeling
methods can automatically learn informative news representations without heavy
effort on manual feature engineering, and they can usually better understand
news content than traditional feature-based methods.

###  2.2. User Modeling

User modeling techniques in news recommendation aim to understand users’
personal interest in news. Similar to news modeling, user modeling methods can
also be roughly classified into two categories, i.e., feature-based and deep
learning-based. Some feature-based methods like CF represent users with their
IDs (Resnick et al., 1994; Das et al., 2007). However, they usually suffer
from the sparsity of user data and cannot model user interest accurately.
Thus, most feature-based methods consider other user information such as click
behaviors on news. For example, Garcin et al. (Garcin et al., 2012) proposed
to use Latent Dirichlet Allocation (LDA) to extract topics from the
concatenation of news title, summary and body. The topic vectors of all
clicked news are further aggregated into a user vector by averaging. There are
also several works that explore to incorporate other user features into user
modeling, such as demographics (Lee and Park, 2007), location (Fortuna et al.,
2010) and access patterns (Li et al., 2011b). However, feature-based user
modeling methods also require an enormous amount of domain knowledge to design
informative user features in specific scenarios, and they are usually
suboptimal in representing user interests.

There are several methods that use neural networks to learn user
representations from users’ click behaviors. For example, Okura et al. (Okura
et al., 2017) proposed to use a GRU network to learn user representations from
clicked news. Wu et al. (Wu et al., 2019c) proposed a personalized attention
network to learn user representations from clicked news in a personalized
manner. Qi et al. (Qi et al., 2021d) proposed a hierarchical user interest
representation method to model the hierarchical structure of user interest.
These methods can automatically learn deep interest representations of users
for personalized news recommendation, which are usually more accurate than
handcrafted user interest features.

###  2.3. Personalized Ranking

On the basis of news and user interest modeling, the next step is ranking
candidate news in a personalized way according to user interest. Most methods
rank news based on their relevance to user interest, and how to accurately
measure the relevance between user interest and candidate news is their core
problem. Some methods measure the user-news relevance based on their
representations. For example, Goossen et al. (Goossen et al., 2011) proposed
to compute the cosine similarity between the Concept Frequency-Inverse
Document Frequency (CF-IDF) features extracted from candidate news and clicked
news, which was further used for personalized candidate news ranking. Okura et
al. (Okura et al., 2017) used the inner product between news and user
embeddings to compute the click scores, and ranked candidate news based on
these scores. Gershman et al. (Gershman et al., 2011) proposed to use an SVM
model for each individual user to classify whether this user will click a
candidate news based on news and user interest features. In several recent
methods, the relevance between candidate news and user interest is modeled in
a fine-grained way by matching candidate news with clicked news. For example,
Wang et al. (Wang et al., 2020c) proposed to match candidate news and clicked
news with a 3-D convolutional neural network to mine the fine-grained
relatedness between their content. However, ranking candidate news and user
interest merely based on their relevance may recommend news that are similar
to those previously clicked by users (Qi et al., 2021d), which may cause the
“filter bubble” problem.

A few methods use reinforcement learning for personalized ranking. Li et al.
(Li et al., 2010a) first explore to model the personalized news recommendation
task as a contextual bandit problem. They proposed a LinUCB approach that
computes the upper confidence bound (UCB) of each arm efficiently in closed
form based on a linear payoff model, which can match news with users’ personal
interest and meanwhile explore making diverse recommendations. DRN (Zheng et
al., 2018) uses a deep reinforcement learning approach to find the interest
matching policy that optimizes the long-term reward. In addition, it uses a
Dueling Bandit Gradient Descent (DBGD) method for exploration. These methods
usually optimize the long-term reward rather than the current click
probability, which has the potential to alleviate the filter bubble problem by
exploring more diverse user interest.

###  2.4. Model Training

Many personalized news recommendation methods employ machine learning models
for news modeling, user modeling and interest matching. How to train these
models to make accurate recommendations is a critical problem. A few methods
train their models by predicting the ratings on news given by users. For
example, the Grouplens (Resnick et al., 1994) system is trained by predicting
the unknown ratings in the user-news matrix. However, explicit feedback such
as ratings is usually sparse on news platforms. Thus, most existing methods
use implicit feedback like clicks to construct prediction targets for model
training. For example, Wang et al. (Wang et al., 2018) formulated the news
click prediction problem as a binary classification task, and use crossentropy
as the loss function for model training. Wu et al. (Wu et al., 2019c) proposed
to employ negative sampling techniques that combine each positive sample with
several negative samples to construct labeled samples for model training.
However, click feedback usually contains heavy noise and may not indicate user
interest, which poses great challenges to learning accurate recommendation
models.

There are only a few methods that consider user feedback beyond click signals
(Wu et al., 2020e, 2021d). For example, Wu et al. (Wu et al., 2020e) proposed
to model click preference with click feedback and model reading satisfaction
based on the personalized reading speed of users, and train the recommendation
model to predict both clicks and user satisfaction. By optimizing objectives
beyond news clicks, these methods are aware of user engagement information and
thereby can better understand user interest. In addition, these methods have
the potential to recommend news articles that are not only clicked by users,
but also indeed satisfy their information needs. Thus, designing engagement-
aware training objectives is useful for news recommender systems to provide
high-quality news suggestions.

###  2.5. Evaluation

Properly evaluating the performance of personalized news recommendation
algorithms is important for developing real-world news recommender systems.
Most existing methods use click-related metrics to measure the accuracy of
recommendation results. Some of them regard the recommendation task as a
classification problem (Wang et al., 2018; Lian et al., 2018; Hu et al.,
2020a), where the performance is evaluated by classification metrics such as
Area Under Curve (AUC) and F1-score. Many other methods use ranking metrics
such as Mean Reciprocal Rank (MRR) and normalized Discounted Cummulative Gain
(nDCG). However, click-based metrics may not indicate user experience. Thus, a
few works explore to use user engagement-based metrics to evaluate the
recommendation performance (Wu et al., 2021d), such as dwell time and dislike,
which can evaluate the performance of recommendation models more
comprehensively.

In most works, the performance of recommendation models is offline evaluated.
However, the data used for offline evaluation is usually influenced by the
recommendation results generated by the predecessor recommendation algorithms,
and the real user feedback on recommendation results cannot be obtained. Only
a few works reported online evaluation results (Wu et al., 2021c), which may
better indicate the real performance of the recommender systems. To fill the
gaps between offline and online experiments, one prior study (Li et al.,
2011a) proposed an unbiased evaluation method of contextual bandit-based news
recommendation methods. However, there still lacks a general method that can
offline evaluate the potentials of various news recommender algorithms in
online environments.

###  2.6. Dataset and Benchmark

Publicly available datasets are important for facilitating researches in the
corresponding fields as well as benchmarking their results and findings.
However, in the personalized news recommendation field most researches are
conducted on proprietary datasets collected from different news platforms,
such as Google News, Microsoft News, Yahoo News, Bing News, etc. There are
only a few datasets that are publicly available for news recommendation
research. Several representative datasets such as plista (Kille et al., 2013),
Adressa (Gulla et al., 2017) and MIND (Wu et al., 2020a) are widely used by
recent studies. The plista dataset is a German news dataset. A newer version
of this dataset is published by the CLEF 2017 NewsREEL (Lommatzsch et al.,
2017) task, and a competition is held based on this data to train and evaluate
news recommender systems. Adressa is a Norwegian dataset that contains not
only click information, but also the dwell time of users and rich context
information of users and news. MIND is a large-scale English news
recommendation dataset with raw textual information of news. In addition, MIND
is associated with a public leaderboard and an open competition, which can
fairly compare the performance of different algorithms. Thus, many recent
researches are conducted on the MIND dataset (Wu et al., 2021c, g, b).

###  2.7. Responsible News Recommendation

Most endeavors on personalized news recommendation focus on improving the
accuracy of recommendation results. In recent years, research on the
responsibility of machine intelligent systems has gained high attention to
help AI techniques better serve humans and avoid their risky and even harmful
behaviors that can lead to negative societal impacts and unethical
consequences (Dignum, 2019). There are many aspects to improve the
responsibility of personalized news recommender systems (Bastian et al.,
2021). For example, since many news recommendation methods are learned on
private user data, it is important to protect user privacy in recommendation
model training and online serving (Qi et al., 2021c). Federated learning
(McMahan et al., 2017) is a privacy-aware machine learning paradigm, which can
empower the construction of privacy-preserving news recommender systems.
Besides optimizing news recommendation accuracy, it is also important to
promote the diversity of news recommendation results, which can satisfy users’
needs on information variety and alleviate the filter bubble problem (Rao et
al., 2013b; Wu et al., 2020d; Raza and Ding, 2021b; Hendrickx et al., 2021).
Moreover, fairness is a critical aspect of responsible news recommendation,
since the recommendation models learned on biased user data may inherit
unwanted biases, which may lead to the prejudice of algorithms and further
unfair recommendation results. To mitigate the unfairness issue of news
recommendation methods, fairness-aware machine learning techniques (Wu et al.,
2021i) can help build inclusive and fair algorithms to provide high-quality
news recommendation services to different groups of users. These research
fields on responsible news recommendation emerging in recent years have the
potential to improve the quality of news recommender systems to serve users in
a more responsible way. However, there lacks a systematic review on
responsible news recommendation in existing survey papers. In this survey, we
first give a comprehensive review on the frontiers of responsible news
recommendation research.

Given the overview above, we then present in-depth discussions on each
mentioned core problem in the following sections.

##  3\. News Modeling

News modeling is a critical step in personalized news recommendation methods
to capture the characteristics of news articles and understand their content.
The techniques for news modeling can be roughly divided into two categories,
i.e., feature-based and deep learning-based. For feature-based methods, news
articles are mainly represented by handcrafted features, while deep learning-
based methods mainly aim to learn hidden news representations from the raw
inputs. Note that although a few methods may employ some deep learning methods
like multi-layer perceptrons to model interactions between sophisticated
handcrafted features, we still categorize them into feature-based ones because
their news representations are not learned from scratch. In addition, some
deep learning-based methods may involve some efforts in feature engineering.
Since their news representation methods mainly focus on incorporating
additional features to enhance deep representations learned from scratch, we
still put them into the deep learning-based category. The details of the two
types of news modeling methods are as introduced follows.

###  3.1. Feature-based News Modeling

Designing informative features to represent news articles is the key problem
in feature-based news modeling methods. As summarized in Fig. 2, there are
mainly four types of features used in news modeling, which are introduced as
follows.

In many CF-based methods, news articles are represented by collaborative
filtering signals such as news IDs (Resnick et al., 1994; Das et al., 2007;
Shan Liu et al., 2016; Xiao et al., 2015; Ji et al., 2016; Mookiah et al.,
2018; Han, 2020). However, on most news websites novel news are published
quickly and old ones will soon vanish. These methods model news in a content-
agnostic manner, which may suffer from the serious cold start problem due to
the difficulty in processing newly generated news. Thus, it is not suitable to
simply represent news articles with their IDs (Domann and Lommatzsch, 2017).

Figure 3. An overview of different types of news features.

Due to the drawbacks of ID-based news modeling, many methods incorporate news
content into news modeling. For instance, Gershman et al. (Gershman et al.,
2011) considered Term Frequency-Inverse Document Frequency (TF-IDF) features
extracted from news texts. In news articles, entities/concepts are usually
more important than other words in understanding news content. Thus, many
methods use the entities/concepts in news texts to represent their content.
For example, Goossen et al. (Goossen et al., 2011) proposed to use Concept
Frequency-Inverse Document Frequency (CF-IDF) to model news content, which is
a variant of TF-IDF that uses the frequency of concepts extracted from WordNet
rather than term frequency. Capelle et al. (Capelle et al., 2012) proposed to
use Synset Frequency–Inverse Document Frequency (SF-IDF) to model news, which
is based on the frequency of synonym sets in WordNet. SF-IDF is extended by
Moerland et al. (Moerland et al., 2013) into SF-IDF+ by additionally
considering the relationships of concepts. They extend the synonym sets of
concepts in news by adding other concepts in WordNet that have relationships
with the included concepts. Based on aforementioned approaches, the family of
CF-IDF is expanded by a set of later works (Hogenboom et al., 2013; Hogenboom
et al., 2014; Capelle et al., 2015; de Koning et al., 2018; Brocken et al.,
2019).

Besides semantic features, some works explore to extract other kinds of
content features to enhance modeling (Lee and Park, 2007; Li et al., 2011b;
Parizi and Kazemifard, 2015). For example, Garcin et al. (Garcin et al., 2012)
proposed to use Latent Dirichlet Allocation (LDA) to extract topics from the
concatenation of news title, summary and main content. Parizi et al. (Parizi
and Kazemifard, 2015) proposed to extract emotion features of sentences in
news as complementary information of TF-IDF features. In their method, the
emotion is represented by the Ekman model that contains 6 emotion categories.
A variant of this method that uses the sentiment orientation (i.e., positive,
neutral and negative) is also developed by Parizi et al. (Parizi et al.,
2016). Beyond news texts, the exploitation of vision-related information such
as the videos of news is also studied in (Luo et al., 2008). These features
can provide complementary information to better understand news content.

In addition to content features, many other genres of features are used for
news modeling. They can be roughly divided into two categories, i.e., property
features and context features. Property features such as categories, locations
and publishers usually reflect intrinsic properties of news. The most widely
used news property feature is category, since it is an important clue for
modeling news content and targeting user interest. For example, Liu et al.
(Liu et al., 2010) proposed to represent news using their topic categories.
However, since the category labels of news often need to be manually annotated
by editors, in some scenarios news may not have off-the-shelf category labels,
Thus, several methods explore to cluster news into categories based on their
content. For instance, in the SCENE (Li et al., 2011b) recommender system,
news articles are clustered in a hierarchical manner based on their topic
features extracted by LDA. By incorporating the categories or clusters of news
into news modeling, the news recommender can be aware of news topics and
provide more targeted recommendation services. Another representative property
feature is news location, which is also widely used to provide users with the
news related to the locations that they are interested in. For example,
Tavakolifard et al. (Tavakolifard et al., 2013) incorporated the geographic
information of news to filter news based on their locations. In addition,
since news from different publishers may have differences in their content and
topics, the information of news publisher is also considered by several
methods to enrich the information for news modeling (Ilievski and Roy, 2013;
Liang et al., 2017).

Table 2. Main features used for news representation. *XF-IDF means TF-IDF and
its variants such as CF-IDF and SF-IDF.

Features for News Modeling | References
---|---
BOW/XF-IDF* |  |  (Goossen et al., 2011)(Capelle et al., 2012)(Moerland et al., 2013)(Capelle et al., 2013)(Hogenboom et al., 2013)(Hogenboom et al., 2014)(Capelle et al., 2015)(de Koning et al., 2018)(Brocken et al., 2019)(Gershman et al., 2011)(Chu and Park, 2009)(Lang, 1995)(Wen et al., 2012)(Nguyen et al., 2015)(Rao et al., 2013a)
---
(Zhang et al., 2017)(Billsus and Pazzani, 2000)(Gabrilovich et al.,
2004)(Cantador and Castells, 2009)(Gu et al., 2014)(Kirshenbaum et al.,
2012)(Kompan and Bieliková, 2010)(Luostarinen and Kohonen, 2013)(Phelan et
al., 2009)(Parizi and Kazemifard, 2015)(Parizi et al., 2016)(Liu et al.,
2021c)(Wei et al., 2021)(Lu and Liu, 2016)
Entity/Keyword |  |  (Goossen et al., 2011)(Capelle et al., 2012)(Moerland et al., 2013)(Capelle et al., 2013)(Hogenboom et al., 2013)(Hogenboom et al., 2014)(Capelle et al., 2015)(de Koning et al., 2018)(Brocken et al., 2019)(Li et al., 2011b)(Gershman et al., 2011)(Tavakolifard et al., 2013)(Ilievski and Roy, 2013)(Lin et al., 2014)(Mookiah et al., 2018)(Li and Li, 2013)(Joseph and Jiang, 2019) (Claypool et al., 1999)(Darvishy et al., 2020)
---
(Rao et al., 2013a)(Zhang et al., 2017)(Billsus and Pazzani, 2000)(Gabrilovich
et al., 2004)(Zheng et al., 2018)(Caldarelli et al., 2016)(Cantador and
Castells, 2009)(Cantador et al., 2011)(Frasincar et al., 2009)(Jugovac et al.,
2018)(Kompan and Bieliková, 2010)(Tran et al., 2010)(Khattar et al.,
2017)(Wang et al., 2020a)
Cluster/Category |  |  (Lee and Park, 2007)(Li et al., 2011b)(Jonnalagedda and Gauch, 2013)(Darvishy et al., 2020)(Epure et al., 2017)(Chu and Park, 2009)(Liu et al., 2010)(Saranya and Sadhasivam, 2012)(Darvishy et al., 2020)(Sottocornola et al., 2018)(Gao et al., 2009)(Li et al., 2014)(Li et al., 2011d)(Viana and Soares, 2017)(Li et al., 2010a)(Zheng et al., 2018)(Caldarelli et al., 2016)
---
(Gu et al., 2014)(Jonnalagedda et al., 2016)(Kompan and Bieliková, 2010)(Sood
and Kaur, 2014a)(Zeleník and Bieliková, 2011)(Liu et al., 2021c)(Gharahighehi
et al., 2020)(Yang et al., 2020)(Symeonidis et al., 2021)(Tiwari et al., 2022)
Topic Distribution |  (Garcin et al., 2012)(Li et al., 2011b)(Noh et al., 2014)(Zihayat et al., 2019)(Li and Li, 2013)(Garcin and Faltings, 2013)(Garcin et al., 2013)(Saranya and Sadhasivam, 2012)(Li et al., 2014)(Li et al., 2011d)(Hsieh et al., 2016)(Li et al., 2010b)(Luostarinen and Kohonen, 2013)(Patankar et al., 2019)(Tran et al., 2010)(Hsieh et al., 2016)
Location |  (Tavakolifard et al., 2013)(Noh et al., 2014)(Yeung and Yang, 2010)(Ilievski and Roy, 2013)(Viana and Soares, 2017)(Kazai et al., 2016)(Wei et al., 2021)
Publisher |  (Ilievski and Roy, 2013)(Liang et al., 2017)(Zheng et al., 2018)(Yang et al., 2020)
Popularity |  (Shan Liu et al., 2016)(Li et al., 2011b)(Gershman et al., 2011)(Jonnalagedda and Gauch, 2013)(Tavakolifard et al., 2013)(Darvishy et al., 2020)(Ilievski and Roy, 2013)(Zihayat et al., 2019)(Chu and Park, 2009)(Darvishy et al., 2020)(Li et al., 2011d)(Jonnalagedda et al., 2016)(Kazai et al., 2016)(Kirshenbaum et al., 2012)(Garcin and Faltings, 2013)(Ilievski and Roy, 2013)
CTR | (Chu and Park, 2009)
Recency |  (Lee and Park, 2007)(Li et al., 2011b)(Gershman et al., 2011)(Tavakolifard et al., 2013)(Darvishy et al., 2020)(Ilievski and Roy, 2013)(Zihayat et al., 2019)(Saranya and Sadhasivam, 2012)(Khattar et al., 2017)(Darvishy et al., 2020)(Li et al., 2011d)(Zheng et al., 2018)(Zeleník and Bieliková, 2011)(Wei et al., 2021)
Novelty |  (Garcin et al., 2013)(Gabrilovich et al., 2004)
Dwell Time |  (Chen et al., 2009)(Gershman et al., 2011)(Yi et al., 2014)(Ilievski and Roy, 2013)(Zihayat et al., 2019)(Zheng et al., 2018)(Ilievski and Roy, 2013)
Time Stamp |  (Ilievski and Roy, 2013)(Epure et al., 2017)(Chu and Park, 2009)(Fortuna et al., 2010)(Xiao et al., 2015)(Yang et al., 2020)
Emotion/Sentiment |  (Parizi and Kazemifard, 2015)(Parizi et al., 2016)
Bias | (Patankar et al., 2019)
Knowledge Graph |  (Joseph and Jiang, 2019)(Zhang et al., 2017)
News/User Graph |  (Lin et al., 2014)(Mookiah et al., 2018)(Li and Li, 2013)(Garcin et al., 2013)(Trevisiol et al., 2014)(Li et al., 2014)(Li et al., 2010b)(Phelan et al., 2011a)(Gharahighehi et al., 2020)(Symeonidis et al., 2021)
Ontology |  (Goossen et al., 2011)(Capelle et al., 2012)(Moerland et al., 2013)(Capelle et al., 2013)(Hogenboom et al., 2013)(Hogenboom et al., 2014)(Capelle et al., 2015)(de Koning et al., 2018)(Brocken et al., 2019)(Wen et al., 2012)(Nguyen et al., 2015)(Rao et al., 2013a)(Shapira et al., 2009)(Gao et al., 2009)(Cantador and Castells, 2009)(Cantador et al., 2011)(Frasincar et al., 2009)
Visual Information | (Luo et al., 2008)

Different from property features that are usually static after news
publishing, context features of news are dynamic. Popularity and recency,
which reflect the attractiveness and freshness of news, are two representative
context features used by existing methods. For instance, MONERS (Lee and Park,
2007) is a news recommender system that represents news articles by news
categories, news importance suggested by providers and the recency of news
articles. Gershman et al. (Gershman et al., 2011) proposed to use four kinds
of features to represent news, i.e., news popularity, news age (recency), TF-
IDF features of words and named entities. Jonnalagedda et al. (Jonnalagedda
and Gauch, 2013) proposed to use the timeline on Twitter to enhance news
modeling. They use the popularity and categories of news on Twitter for news
representation. News recency only considers the time interval between the
publishing and display of news, while time stamp of news display can provide
finer-grained information, such as seasons, months, days and the time in a
day. Thus, several approaches incorporate the time stamp of news impression
(Chu and Park, 2009; Fortuna et al., 2010; Ilievski and Roy, 2013; Xiao et
al., 2015; Epure et al., 2017). For example, Ilievski et al. (Ilievski and
Roy, 2013) proposed to incorporate the weekday and the hour of a news
impression in news modeling. In addition to the context features mentioned
above, several methods also explore to use weather (Yeung and Yang, 2010),
click-through rate (CTR) (Chu and Park, 2009), and fact/opinion bias (Patankar
et al., 2019) to enrich the representations of news.

Some hybrid methods consider both news IDs and additional features in news
modeling (Lommatzsch, 2014). For example, NewsWeeder (Lang, 1995) represents
news articles by their IDs and bag-of-word features. Claypool et al. (Claypool
et al., 1999) proposed to use news IDs and keywords to model news. Liu et al.
(Liu et al., 2010) proposed to represent news using their IDs and topic
categories. Saranya et al. (Saranya and Sadhasivam, 2012) proposed to
represent news by their IDs, topics, click frequency and the weights of a news
belonging to different categories. Using the combination of ID-based and
content-based news modeling techniques can mitigate the cold-start problem of
news to some extent, and have been widely explored by integrating other
information like news property features (Wen et al., 2012; Darvishy et al.,
2020), news sessions (Sottocornola et al., 2018), ontology (Cantador et al.,
2011; Nguyen et al., 2015; Rao et al., 2013a; Shapira et al., 2009; Gao et
al., 2009) and knowledge graphs (Zhang et al., 2017).

To draw a big picture of feature-based news modeling methods, we summarize the
major features they used in Table 2.

###  3.2. Deep learning-based News Modeling

With the development of deep learning techniques, in recent years many methods
employ neural networks to automatically learn news representations. Instead of
using handcrafted features like TF-IDF to represent news content, most of them
use neural NLP techniques to learn news representations from news texts. For
example, Okura et al. (Okura et al., 2017) proposed an embedding-based news
recommendation (EBNR) method that uses a variant of denoising autoencoders to
learn news representations from news texts. RA-DSSM (Kumar et al., 2017b) is a
neural news recommendation approach which incorporates a similar architecture
as DSSM (Huang et al., 2013). It first builds the representations of news
using the doc2vec (Le and Mikolov, 2014) tool, then uses a two-layer neural
network to learn hidden news representations. This method is also adopted by
(Kumar et al., 2017c). 3-D-CNN (Kumar et al., 2017a) represents news by the
word2vec (Mikolov et al., 2013) embeddings of their words, which is further
considered by (Zhang and Ling, 2021). However, it is difficult for these
methods to mine the semantic information in news texts with traditional neural
NLP models.

Many later approaches use more advanced neural NLP models for text modeling,
such as CNN (Wang et al., 2020c; Zhang et al., 2021a) and self-attention (Wu
et al., 2019e). For instance, WE3CN (Khattar et al., 2018) uses 2D CNN models
to learn representations of news. NPA (Wu et al., 2019c) uses CNN to generate
contextual representations of words in news titles, and use a personalized
attention network to form news representations by selecting important words in
a personalized manner. NRMS (Wu et al., 2019e) learns word representations
with a multi-head self-attention network, and uses an additive attention
network to form news representations. Similar news modeling method is also
used by many later works (Wu et al., 2020e, d, 2021i, 2021d, 2021g). NRNF (Wu
et al., 2020b) uses self-attention to model the contexts of words in news
title and body, and it uses an interactive attention network to model the
relatedness between title and body. A similar co-attention mechanism is used
by (Mao et al., 2021) to model the interactions between news title and
abstract. FedRec (Qi et al., 2020) learns news representations from news
titles via a combination of CNN and multi-head self-attention networks. These
methods usually learn news representations based on shallow text models and
non-contextualized word embeddings such as GloVe (Pennington et al., 2014),
which may be insufficient to capture the deep semantic information in news.
WG4Rec (Shi et al., 2021) introduces a word-graph based news text modeling
method. It constructs a word graph based on semantic similarity, co-occurrence
and news co-click, and learns word embeddings through a GNN model. These
methods can enhance news text understanding with various neural architectures.
However, these models are still rather shallow and may not be strong enough in
capturing the deep semantic information in news texts.

In recent years, big and powerful pre-trained language models (PLMs) such as
BERT (Devlin et al., 2019) have been greatly successful in NLP, and a few
recent works explore to empower news modeling with PLMs (Wu et al., 2021c;
Xiao et al., 2021; Jia et al., 2021; Zhang et al., 2021b, c; Raza and Ding,
2021a; Wu et al., 2021j; Yi et al., 2021b). For example, PLM-NR (Wu et al.,
2021c) uses different PLMs to empower English and multilingual news
recommendation, and the online flight results in Microsoft News showed notable
performance improvement. UNBERT (Zhang et al., 2021c) incorporates the
concatenation of news texts as the input of a BERT model. The findings in
these works imply the effectiveness of large PLMs in empowering text
understanding in news recommendation.

Instead of merely modeling semantic information in news texts, several methods
study to use entities or keywords in news texts to enhance news modeling by
introducing complementary knowledge and commonsense information. A direct way
is regarding entities as texts and combining them with news text modeling (Ji
et al., 2021a). For instance, Gao et al. (Gao et al., 2018) proposed a
knowledge-aware news recommendation approach with hierarchical attention
networks. In their method, a word attention network is used to learn word-
based news representations by using the embeddings of keywords as attention
queries, and these representations are concatenated with both entity
embeddings and the average embeddings of the entities in their contexts. An
item attention network is used to aggregate these three kinds of news
representations by modeling their informativeness. DAN (Zhu et al., 2019)
learns news representations from news titles and entities via two parallel CNN
networks with max pooling operations. Saskr (Chu et al., 2019) builds news
representations from news titles and bodies based on the average word
embeddings of their entities. DNA (Zhang et al., 2019a) learns news
representations from the news body, news ID and the elements (entities and
keywords). More specifically, the sentences in a news body are transformed
into their embeddings via doc2vec (Le and Mikolov, 2014), and then are
aggregated into a unified one via a sentence-level candidate-aware attention
network. Each news element is represented by averaging the embeddings of its
words, and elements representations are synthesized together via an element-
level candidate-aware attention network. The embeddings of the ID, texts, and
elements of each piece of news are concatenated together into a unified news
representation. HieRec (Qi et al., 2021d) uses text self-attention and entity
self-attention to model the contexts in news titles and the relations between
entities in news texts, respectively. These methods can easily unify the use
of texts and knowledge entities, but they cannot effectively exploit the
relatedness between entities.

Another way to exploit entity information is incorporating knowledge graph
embeddings (Wang et al., 2018; Liu et al., 2019; Sheu and Li, 2020; Tran et
al., 2021; Sun et al., 2021b). For example, DKN (Wang et al., 2018) learns
news representations from the titles of news and the entities within titles
via a knowledge-aware CNN. The representations of entities are learned from a
knowledge graph using the TransD (Ji et al., 2015) knowledge graph embedding
algorithm. Liu et al. (Liu et al., 2019) proposed to construct a news-relevant
knowledge graph on the basis of the Microsoft Satori knowledge graph by
extracting additional knowledge entities and topic entities from news and
connecting entities in the same news, entities clicked by the same user and
entities appearing in the same browsing session to enrich the relations
between entities in the knowledge graph. They combine the entity embeddings
learned by TransE (Bordes et al., 2013) with the news text embeddings learned
by LDA and DSSM. CAGE (Sheu and Li, 2020; Sheu et al., 2021) constructs
subgraphs of KG by using one-hop neighbors of entities, and uses the TransE
embeddings of entities as complements to text embeddings learned by CNN.
However, these knowledge graph embeddings mainly condense low-level
interactions between entities. To enhance the modeling of rich entity
relatedness, TEKGR (Lee et al., 2020) enriches the knowledge graph with
topical relations between entities. It predicts the topic of news based on
texts and concepts, and uses the predicted topic to enrich the knowledge graph
and learn topic enriched knowledge representations of news with graph neural
networks. KRED (Liu et al., 2020) first learns entity embeddings from
knowledge graph with graph attention networks, then incorporates additional
entity features such as frequency, category and position, and finally selects
entities according to the texts representations of news. KIM (Qi et al.,
2021a) incorporates a knowledge-aware interactive news modeling method that
can model the relations between the entities and their neighbors of clicked
news and candidate news through graph co-attention networks. KOPRA (Tian et
al., 2021) only uses the TransE embeddings of knowledge entities to represent
news, and it uses a recurrent graph convolution network to learn hidden entity
representations. These methods can encode richer knowledge information of news
than pure text-based methods to empower news recommendation.

To better model the characteristics of news articles, several methods explore
to incorporate other types of news information beyond texts into news
modeling. Among them, topic categories and tags are widely considered by
existing methods (Park et al., 2017; Zhang et al., 2018; Wu et al., 2019a, b;
Han et al., 2021). For example, DeepJoNN (Zhang et al., 2018) learns news
representations from news IDs, categories, keywords and entities via a
character-level CNN. Park et al. (Park et al., 2017) proposed a neural news
recommendation method based on LSTM. They use a proprietary corpus to train a
doc2vec (Le and Mikolov, 2014) model to encode news articles into their vector
representations, and use an LSTM network to generate user representations from
the representations of news. In addition, they incorporate the categories of
news into news representations, which are predicted by a CNN (Kim, 2014)
model. TANR (Wu et al., 2019a) learns news representation from news titles via
a combination of CNN and attention network, which is also used in (Wu et al.,
2019d; Yi et al., 2021a). Moreover, TANR incorporates an auxiliary news topic
prediction task to learn topic-aware news representations. NAML (Wu et al.,
2019b) is a news recommendation method with attentive multi-view learning,
which incorporates different kinds of news information as different views of
news. In this method, news titles, bodies, categories and subcategories are
processed by different models, and their embeddings are further aggregated
together into a unified one via a view-level attention network. A similar
method is also used by (Zhang et al., 2021d; Wu et al., 2021b) to model
candidate news. LSTUR (An et al., 2019) uses a combination of CNN and
attention network to process news titles, and incorporates categories and
subcategories by applying a non-linear transformation to their embeddings.
CHAMELEON (Gabriel De Souza et al., 2019; de Souza Pereira Moreira et al.,
2018) learns news representations from news bodies by using CNN with different
kernel sizes, and these textual representations are fused with news metadata
features such as topics, categories and tags using a fully connected layer. It
also predicts the metadata features of news via auxiliary tasks.

In addition to topical information, several methods consider other types of
content information of news. For example, SentiRec (Wu et al., 2020d)
considers the sentiment orientation of news to learn sentiment-aware news
representations. It uses the VADER (Hutto and Gilbert, 2014) algorithm to
compute real-valued sentiment scores of news. MM-Rec (Wu et al., 2021f) uses a
visiolinguistic model ViLBERT (Lu et al., 2019) to learn news multi-modal
representations from both news texts and images. IMRec (Xun et al., 2021)
models the rich visual impression information of news such as texts, image
regions, the arrangement of different fields, and spatial positions of
different words on the impression. These methods can usually learn more
accurate news representations by characterizing their content in multiple
aspects.

Another major group of additional information is context features, such as
popularity and positions. For example, PP-Rec (Qi et al., 2021b) uses both
news title, entities and news popularity information in news modeling. It uses
gating mechanisms to synthesize the near-real-time CTR, recency and popularity
predicted from news title into a unified news popularity score. TSHGNN (Ji et
al., 2021b) incorporates the active time of users on a news page into the
modeling of news texts. DCAN (Meng et al., 2021) uses the time from news
publishing and near-real-time CTR to model the current positions of news
articles in their lifecycles. CTX (Cho et al., 2021) studies the exploitation
of CTR, Popularity, and Freshness features. The results show that these
context features may even have a stronger impact than personalized interest
signals on click prediction. DebiasRec (Yi et al., 2021a) uses CNN and
attention network to learn news content representations from news titles, and
learns news bias representations from the size and positions of news displayed
on websites with a bias model. These methods can usually better understand
users’ interaction patterns with news by incorporating additional context
information. However, some news features (e.g., near-real-time CTR) may not be
available in some real-world news recommender systems, which hinders the
exploitation of these features.

Table 3. Comparison of different methods on news modeling.

Method | Year | Information Used | Model
---|---|---|---
EBNR (Okura et al., 2017) | 2017 | Body | Autoencoder
RA-DSSM (Kumar et al., 2017b) | 2017 | Title+Body | Doc2vec+NN
Khattar et al. (Kumar et al., 2017c) | 2017 | Title+Body | Doc2vec+NN
3-D-CNN (Kumar et al., 2017a) | 2017 | Title+Body | Word2vec
WE3CN (Khattar et al., 2018) | 2018 | Title+Body | 2-D CNN
NPA (Wu et al., 2019c) | 2019 | Title | CNN+Personalized Attention
NRMS (Wu et al., 2019e) | 2019 | Title | Self-Attention+Attention
NRHUB (Wu et al., 2019d) | 2019 | Title | CNN+Attention
DAINN (Zhang et al., 2019b) | 2019 | Body | CNN+Dynamic Topic Model
FIM (Wang et al., 2020c) | 2020 | Title | Dilated CNN
NRNF (Wu et al., 2020b) | 2020 | Title | Transformer+Attention
FedRec (Qi et al., 2020) | 2020 | Title | CNN+Self-Attention+Attention
CPRS (Wu et al., 2020e) | 2020 | Title+Body | Self-Attention+Attention
UniRec (Wu et al., 2021g) | 2021 | Title | Self-Attention+Attention
FeedRec (Wu et al., 2021d) | 2021 | Title | Transformer+Attention
FairRec (Wu et al., 2021i) | 2021 | Title | Transformer+Attention
EEG (Zhang et al., 2021d) | 2021 | Title+Abstract+Body | CNN+Attention
AMM (Zhang et al., 2021b) | 2021 | Title+Abstract+Body | PLM
RMBERT (Jia et al., 2021) | 2021 | Title | PLM
UNBERT (Zhang et al., 2021c) | 2021 | Title | PLM+Attention
PLM-NR (Wu et al., 2021c) | 2021 | Title | PLM+Attention
SFI (Zhang et al., 2021a) | 2021 | Title | CNN+Attention
TempRec (Wu et al., 2021e) | 2021 | Title | Transformer
WG4Rec (Shi et al., 2021) | 2021 | Title+Word Graph | GNN+Attention
CNE-SUE (Mao et al., 2021) | 2021 | Title+Abstract | LSTM+Self-Attention+Co-Attention
DKN (Wang et al., 2018) | 2018 | Title+Entity | KCNN
Gao et al. (Gao et al., 2018) | 2018 | Body+Entity | Attention
DAN (Zhu et al., 2019) | 2019 | Title+Entity | CNN
DNA (Zhang et al., 2019a) | 2019 | Body+Element+ID | Doc2vec+Candidate-Aware Attention+ID Embedding
Saskr (Chu et al., 2019) | 2019 | Entity | Entity Embedding
Liu et al. (Liu et al., 2019) | 2019 | Title+Entity | Entity Embedding+Attention
TEKGR (Lee et al., 2020) | 2020 | Title+Entity | Entity Embedding+Candidate-aware Attention
CAGE (Sheu and Li, 2020) | 2020 | Title+Entity | CNN+Entity Embedding
KRED (Liu et al., 2020) | 2020 | Title+Entity+Entity Context Feature | Attention
HieRec (Qi et al., 2021d) | 2021 | Title+Entity | Transformer+Attention
KIM (Qi et al., 2021a) | 2021 | Title+Entity | CNN+Transformer+Co-Attention+Graph Co-Attention
KOPRA (Tian et al., 2021) | 2021 | Entity | TransE+Recurrent Graph Convolution
Park et al. (Park et al., 2017) | 2017 | Title+Body+Query+Category | Doc2vec
DeepJoNN (Zhang et al., 2018) | 2018 | Keywords/Entities+Category+ID | Char CNN
TANR (Wu et al., 2019a) | 2019 | Title+Category | CNN+Attention+Topic Prediction
LSTUR (An et al., 2019) | 2019 | Title+Category+Subcategory | CNN+Attention
NAML (Wu et al., 2019b) | 2019 | Title+Body+Category+Subcategory | CNN+Attention
CHAMELEON (Gabriel De Souza et al., 2019) | 2019 | Body+Metadata+Context Features | CNN+Attribute Prediction
SentiRec (Wu et al., 2020d) | 2020 | Title+Sentiment | Self-Attention
PP-Rec (Qi et al., 2021b) | 2021 | Title+Entity+CTR+Recency | Self-Attention+Co-Attention+Gating
CTX (Cho et al., 2021) | 2021 | Title+CTR+Popularity+Freshness | Add on Existing Methods
MM-Rec (Wu et al., 2021f) | 2021 | Title+Image | ViLBERT
IMRec (Xun et al., 2021) | 2021 | Title+category+Image+Spatial Position | Pre-trained CNN+Memory Network+Self-Attention
DebiasRec (Yi et al., 2021a) | 2021 | Title+Position+Size | CNN+Attention+Bias Embedding
User-as-Graph (Wu et al., 2021b) | 2021 | Title+Category+Subcategory+Entity | Transformer+Attention
AGNN (Ji et al., 2021a) | 2021 | Title+Entity | CNN
TSHGNN (Ji et al., 2021b) | 2021 | Title+Entity+Active Time | CNN
CUPMAR (Tran et al., 2021) | 2021 | Title+Entity+Body+Category+Subcategory | Self-Attention+Attention
KG-LSTUP (Sun et al., 2021b) | 2021 | Title+Entity+Abstract+Category+Subcategory | LSTM+CNN+Attention
DCAN (Meng et al., 2021) | 2021 | Title+Body+Entity+Time from Publishing+CTR | CNN
D2NN (Raza and Ding, 2021a) | 2021 | Title+Abstract+Category+Subcategory | BERT+CNN+Attention
EENR (Han et al., 2021) | 2021 | Title+Event+Category+Event Type Graph | LSTM+Attention+Node2Vector
IGNN (Qian et al., 2019) | 2019 | Title+Entity+User-News Graph | KCNN+GNN
INNR (Ren et al., 2019) | 2019 | Heterogeneous Graph | Node2vec
GNewsRec (Hu et al., 2020a) | 2020 | Title+Entity+Heterogeneous Graph | CNN+GNN
GERL (Ge et al., 2020) | 2020 | Title+Category+User-News Graph | Transformer+GAT
MVL (Santosh et al., 2020) | 2020 | Title+Body+Category+User-News Graph | CNN+Attention+GAT
GNUD (Hu et al., 2020b) | 2020 | Title+Entity+User-News Graph | CNN+Disentangled GCN
GBAN (Ma et al., 2021) | 2021 | Keywords+Tag+Category+Heterogeneous Graph | CNN+DeepWalk
|  |  |

There are a few methods that learn news representations from graphs. For
example, IGNN (Qian et al., 2019) uses KCNN (Wang et al., 2018) to learn text-
based news representations from news titles, and learn graph-based news
representations from the user-news graph. GERL (Ge et al., 2020) learns news
title representations with a combination of multi-head self-attention and
additive attention networks, and combines title representations with the
embeddings of news categories. MVL (Santosh et al., 2020) uses a content view
to incorporate news title, body and category, and uses a graph view to enhance
news representations with their neighbors on the user-news graph. In addition,
it uses a graph attention network to enhance representations of news by
incorporating the information of their first- and second-order neighbors on
the user-news graph. GNUD (Hu et al., 2020b) uses the same news encoder as DAN
to learn text-based news representations, and uses a graph convolution network
(GCN) with a preference disentanglement regularization to learn disentangled
news representations on user-news graphs. In addition to the user-news graphs
used by the above methods, a few methods incorporate heterogeneous graphs that
condense richer collaborative information (Ren et al., 2019; Hu et al., 2020a;
Ma et al., 2021). For example, GNewsRec (Hu et al., 2020a) is a hybrid
approach which considers graph information of users and news as well as news
topic categories. It also uses the same architecture with DAN to learn text-
based news representations, and uses a two-layer graph neural network (GNN) to
learn graph-based news representations from a heterogeneous user-news-topic
graph. These methods can exploit the high-order information on graphs to
enhance news modeling. However, it is difficult for these methods to handle
newly generated news with few connections to existing nodes on the old graph
used for training.

To help better understand the relatedness and differences between the methods
reviewed above, we summarize the information and models they used for learning
news representations in Table 3. Next, we provide several discussions on the
aforementioned methods for news modeling.

###  3.3. Discussions on News Modeling

####  3.3.1. Feature-based News Modeling

In feature-based news modeling methods, mining textual information of news is
critical for representing news content. Many methods incorporate BOW/TF-IDF
features or their variants to represent news texts, which are also popular in
the NLP field. In addition, topic models like LDA are employed by various
methods to extract topics from texts. This is probably because topic models
are capable of mining the topic distributions of news articles and can also
provide useful clues for inferring user interest on different topics.
Moreover, since users may focus more on the entities or keywords in news, they
are considered by many methods to summarize the content and topic of news, and
can also be useful links to find similar news or map news on knowledge graphs.
Especially, some methods also use ontology such as Wikipedia to extract entity
features to represent them more accurately.

Besides the texts of news, many methods utilize other information of news. For
instance, the categories or clusters of news are popular news features to help
model news content. In addition, several dynamic features of news are also
widely employed in feature-based news modeling methods, such as popularity and
recency. Since many users may pay more attention to popular events and news
usually vanish quickly, incorporating news popularity and recency can help
build more informative news representations. Besides, several environmental
factors, such as locations and time are also utilized by several methods. This
is because considering locations of news can provide news related to users’
neighbors, and using the timestamps of news may be useful for providing time-
aware news services.

A few methods also study incorporating other interesting features. For
example, the sentiment information of news is useful for news understanding,
because users may have different tastes on the sentiment of news. The bias of
news may also need to be taken into consideration, because recommending news
with biased opinions and facts may hurt user experience and the reputation of
news platforms. Finally, although several non-personalized news recommendation
methods have used news images to build news representations (Lommatzsch et
al., 2018), few personalized ones consider the visual information of news,
which is very useful for news modeling.

Although feature-based news modeling methods have comprehensive coverage of
various news information, they usually require a large amount of domain
knowledge for feature design. In addition, handcrafted features are usually
not optimal in representing the textual content of news due to the absence of
the contexts and orders of words.

####  3.3.2. Deep Learning-based News Modeling

Among all the reviewed methods, only two methods, i.e., DNA (Zhang et al.,
2019a) and DeepJoNN (Zhang et al., 2018), directly incorporate the embeddings
of news IDs. This is probably because of the short lifecycle of news articles
and the quick generation of novel news, which make the coverage of news IDs in
the training set very limited. Thus, it is very important to understand news
from their content.

News text modeling is critical for news understanding. Most methods use news
titles to model news since news titles, because news titles usually have
decisive influence on users’ click behaviors. Several methods such as EBNR
(Okura et al., 2017), NAML (Wu et al., 2019b) and CPRS (Wu et al., 2020e) use
news bodies to enhance news representations, since news bodies are contain
more detailed information of news. In existing methods, CNN is the most
frequently used architecture for text modeling. This is because local contexts
in news articles are important for modeling news content, and CNN is effective
and efficient in capturing local contexts. In addition, since different news
information may have different informativeness in modeling news content and
user interest, attention mechanisms are also widely used to build news
representations by selecting important features. With the success of
Transformer in NLP, many methods also use Transformer-like architectures for
news modeling, such as NRMS (Wu et al., 2019e) and CPRS (Wu et al., 2020e). In
addition, a few methods use pre-trained language or and visiolinguistic models
to empower news modeling (Wu et al., 2021c, f). These advanced NLP techniques
can greatly improve news content understanding, which is very important for
personalized news recommendation. However, these methods mainly aim to capture
the semantic information of news and may not be aware of the knowledge and
commonsense information encoded in news.

To address this issue, many methods incorporate news entities into news
modeling to learn knowledge-aware news representations (Liu et al., 2021a).
Some methods such as DAN (Zhu et al., 2019) directly use entity texts to
represent entities, while several other methods like DKN (Wang et al., 2018)
use knowledge graph embeddings to represent entities. These entity
representations are usually combined with representations learned from news
texts to better model news content. However, there are many new entities and
concepts emerging in news and it may be difficult to accurately represent them
with off-the-shelf knowledge bases.

Several methods incorporate the topic categories of news into news modeling,
because news topics are very useful for understanding news content and
inferring user interest. Considering the scenarios that some news articles are
not labeled with topic categories, some methods such as TANR (Wu et al.,
2019a) and CHAMELEON (Gabriel De Souza et al., 2019) also adopt auxiliary
tasks by predicting news topic categories to encode topic information into
news representations. In addition, a few methods study using other kinds of
news features such as sentiment (Wu et al., 2020d), popularity (Cho et al.,
2021), recency (Qi et al., 2021b), which can help better understand the
characteristics of news. However, some additional news features (e.g.,
category and CTR) may be unavailable in certain scenarios, which limits the
application of these methods.

There are also a few methods that explore to enhance news modeling with graph
information (Ge et al., 2020; Hu et al., 2020a). These methods can incorporate
the high-order information on user-news bipartite graphs (Qian et al., 2019;
Ge et al., 2020; Hu et al., 2020b; Santosh et al., 2020) or more complicated
heterogeneous graphs (Ren et al., 2019; Hu et al., 2020a), which can provide
useful contexts on understanding the characteristics of news for news
recommendation. However, since the graphs used in these methods are static,
they may have some difficulties in accurately representing newly published
news.

In summary, by reviewing news modeling techniques used in existing news
recommendation methods, we can see that news modeling is still a quite
challenging problem in news recommendation due to the variety, dynamic, and
timeliness of online news information.

##  4\. User Modeling

User modeling is also a critical step in personalized news recommender systems
to infer users’ personal interests in news. It is usually important for user
modeling algorithms to understand users from their behaviors (Wu et al.,
2019a). An example user modeling framework in personalized news recommendation
is shown in Fig. 4. We can see that user modeling is based on the modeling of
news that users have interactions with, and it introduces additional user
features to achieve better personalized user understanding. The techniques for
user modeling in existing news recommendation methods can also be classified
into feature-based ones and deep learning-based ones. Feature-based user
modeling techniques mainly rely on manually designed user modeling rules or
heuristic patterns to represent user interest. By contrast, deep learning-
based methods usually focus on automatically finding useful patterns from user
behaviors to infer user interest. The details of the two kinds of user
modeling methods are introduced in the following sections.

Figure 4. An example framework of user modeling.

###  4.1. Feature-based User Modeling

Feature-based user modeling methods use handcrafted features to represent
users. Similar to news modeling, in CF-based methods users are also
represented by their IDs (Resnick et al., 1994; Das et al., 2007). However,
ID-based user modeling methods usually suffer from the data sparsity. Thus,
most methods consider the behaviors of users such as news clicks to model
their interest. An intuitive way is to use the features of clicked news to
build user features. For example, Goossen et al. (Goossen et al., 2011) used
the CF-IDF features of clicked news to represent user interest. Capelle et al.
(Capelle et al., 2012) proposed to use the SF-IDF features of clicked news for
user modeling. Garcin et al. (Garcin et al., 2012) proposed to model users by
aggregating the LDA features of all clicked news into a user vector by
averaging. However, it is difficult for these methods to model users
accurately when their news click behaviors are sparse.

Besides news features, many methods consider other supplementary information
of users in user modeling. For instance, in the MONERS (Lee and Park, 2007)
recommender system, users are clustered into segments, and the preferences of
user segments on news categories and news articles are used to represent
users. In addition, the demographics of users, such as age, gender and
profession, are also useful information for user modeling because users in
different demographic groups usually have different preferences on news. Thus,
user demographic features are incorporated by several methods (Lee and Park,
2007; Yeung and Yang, 2010; Ilievski and Roy, 2013). For instance, Yeung et
al. (Yeung and Yang, 2010) proposed to use the age, gender, occupation status
and social economic grade of users to help identify their different
preferences on news in different categories. Chu et al. (Chu and Park, 2009)
used the age and gender categories of users to model their characteristics.
Besides, the location information of users is also very useful for accurate
user modeling, and it has been used by several location-aware news
recommendation methods (Fortuna et al., 2010; Noh et al., 2014). However, some
kinds of user features such as locations and demographics are privacy-
sensitive, and many users may not provide their accurate personal information.

Since news clicks may not necessarily indicate user interests, several methods
also consider other kinds of user behaviors or feedback. For example, Gershman
et al. (Gershman et al., 2011) proposed to represent users by the news they
carefully read (regarded as positive news), rejected, and scrolled (both are
regarded as negative news). In addition, users’ dwell time on clicked news is
also an important indication of user interest, and Yi et al. (Yi et al., 2014)
studied to use dwell time as the weights of clicked news for user modeling.
Besides these user behaviors, several other kinds of user behavior information
such as access patterns, are utilized by a few methods (Li et al., 2011b;
Saranya and Sadhasivam, 2012) to capture the users’ habits on news reading.

Several methods also consider graph information (e.g., news-user graphs) in
user modeling (Gharahighehi et al., 2020). For example, Li et al. (Li and Li,
2013) proposed a news personalization method by using hypergraph to model
various high-order interactions between different news information, where
users are represented by subgraphs of the hypergraph. Garcin et al. (Garcin et
al., 2013) proposed to use context trees for user modeling. They constructed
context trees based on the sequence of articles, the sequence of topics and
the distribution of topics. Trevisiol et al. (Trevisiol et al., 2014) proposed
to build a browsing graph from the news browsing histories of users on Yahoo
News. Joseph et al. (Joseph and Jiang, 2019) proposed to represent users by
regarding the clicked news as subgraphs of a knowledge graph, which are
constructed via entity linking. These methods can consider the high-order
information on graphs to help understand user behaviors, which can improve
user modeling.

A few methods combine user IDs with other user features in user modeling
(Lommatzsch, 2014). For example, NewsWeeder (Lang, 1995) used user IDs and the
bag-of-words features of clicked news to represent users. Claypool et al.
(Claypool et al., 1999) used user IDs and keywords of clicked news for user
modeling Liu et al. (Liu et al., 2010) proposed to represent users using their
IDs and user interest features predicted by a Bayesian model. These methods
can mitigate the drawbacks of ID-based user modeling and meanwhile incorporate
useful personal information encoded by user IDs.

Considering the evolutionary characteristics of user interest, some methods
model both long-term and short-term user interests (Cantador et al., 2011; Li
et al., 2014). NewsDude (Billsus and Pazzani, 2000) may be one of the earliest
methods that consider long short-term user interests. In this approach, users
are represented by a hybrid model, which models short-term interest of users
based on recently browsed news, and models long-term user interest by sorting
words of news in each category with respect to their TF-IDF values and
selecting the top ranked words. Li et al. (Li et al., 2011d) proposed LOGO,
which is a news recommendation method that models both long-term and short-
term user interests. LOGO uses a weighted summation of the topic distributions
of news clicked by users to indicate long-term user interest, and it uses the
topic distribution of the latest clicked news as the short-term user interest.
Viana et al. (Viana and Soares, 2017) proposed another news recommendation
method based on long short-term user interest. In their method, the long-term
interest of users is represented by the frequency of a specific tag being read
by this user, and short-term interest is represented by several recently
clicked news. Different from other methods that only consider short-term or
long-term user interests, these methods can better model the evolution of user
interests by capturing long short-term user interests.

To help readers better understand feature-based user modeling methods in
personalized news recommender systems, we summarize the additional user
features (ID and news features are excluded) used in these methods in Table 4.

Table 4. Additional features used for user representation. *ID/textual
features of clicked news are excluded because they are incorporated by most
methods.

Features for User Representation* | References
---|---
Demographic |  (Lee and Park, 2007)(Yeung and Yang, 2010)(Ilievski and Roy, 2013)(Chu and Park, 2009)(Li et al., 2010a)(Wei et al., 2021)
Cluster/Segment |  (Lee and Park, 2007)(Yeung and Yang, 2010)(Zheng et al., 2018)(Darvishy et al., 2020)(Manoharan and Senthilkumar, 2020)
Tag/Keyword |  (Jonnalagedda and Gauch, 2013)(Yeung and Yang, 2010)(Darvishy et al., 2020)(Gao et al., 2009)(Billsus and Pazzani, 2000)(Cantador et al., 2011)(Jonnalagedda et al., 2016)(Sood and Kaur, 2014a)(Tiwari et al., 2022)
Location |  (Fortuna et al., 2010)(Yeung and Yang, 2010)(Tavakolifard et al., 2013)(Noh et al., 2014)(Ilievski and Roy, 2013)(Viana and Soares, 2017)(Li et al., 2010a)(Kazai et al., 2016)(Chu and Park, 2009)(Wei et al., 2021)
Access Pattern |  (Li et al., 2011b)(Saranya and Sadhasivam, 2012)(Wei et al., 2021)
Behaviors on Other Platforms |  (Li et al., 2010a)(Gu et al., 2014)(Bai et al., 2017)(Hsieh et al., 2016)(Kazai et al., 2016)(Li et al., 2010b)(Phelan et al., 2011a)(Phelan et al., 2009)(Hsieh et al., 2016) (Lian et al., 2018)

###  4.2. Deep Learning-based User Modeling

In recent years, many personalized news recommendation methods use deep
learning techniques for user modeling to remove the need of manual feature
engineering. Most existing methods infer user interests from historical news
click behaviors. Several methods focus on aggregating the representations of
historical clicked news (Han et al., 2021). For example, Khattar et al. (Kumar
et al., 2017c) used the summation of clicked news representations weighted by
an exponential discounting function, where more recent clicks gain higher
weights. NAML (Wu et al., 2019b) and KRED (Liu et al., 2020) learn user
representations from the representations of clicked news using a news-level
attention network, and AMM (Zhang et al., 2021b) also uses attention network
to aggregate different information of clicked news and candidate news for user
modeling. DKN (Wang et al., 2018) learns user representations from the
representations of clicked news via a candidate-aware attention network, i.e.,
computing the attention weight of each clicked news according to its relevance
to candidate news. The candidate-aware attention mechanism is also used by
TEKGR (Lee et al., 2020) for user modeling. Liu et al. (Liu et al., 2019) use
a simple time-decayed averaging of the embeddings of clicked news to build the
user embedding. MM-Rec (Wu et al., 2021f) uses a crossmodal candidate-aware
attention network that selects clicked news based on their crossmodal
relatedness with candidate news for user modeling. HieRec (Qi et al., 2021d)
uses a hierarchical user interest representation method that first models
subtopic-level user interest from the news within the same subtopic, then
aggregates subtopic-level interest representations into coarse-grained topic-
level user interest representations, and finally synthesizes topic-level
interest representations into an over interest representation. DebiasRec (Yi
et al., 2021a) uses a bias-aware user modeling module to learn debiased user
interest representations by incorporating the influence of presentation bias
information on click behaviors into attentive behavior aggregation. These
methods can select important click behaviors for user modeling. However, the
relations among different clicked news, which provide rich contexts of
behaviors that are useful to user modeling, cannot be modeled by these
methods.

Therefore, many methods consider the contexts of news click behaviors.
Recurrent neural network (RNN) is a popular choice to model the sequential
dependency between different clicked news (Okura et al., 2017; Kumar et al.,
2017b; Zhu et al., 2019; Raza and Ding, 2021a). For example, EBNR (Okura et
al., 2017) learns representations of users from the representations of their
browsed news via a GRU network. RA-DSSM (Kumar et al., 2017b) uses a bi-
directional long short-term memory (Bi-LSTM) network to process the historical
news click sequence, and then use a news-level attention network to form a
user representation. DAN (Zhu et al., 2019) learns user representations from
clicked news using a combination of attentive LSTM and candidate-aware
attention, which generate user historical sequential embedding and user
interest embedding, respectively. The advantage of RNN-based user models is
their strong ability in modeling user interest dynamics. However, they are
somewhat weak in capturing the global interest information of users. In
addition, as pointed by (Wu et al., 2021e), news recommendation may not be
suitable to be modeled as a conventional sequential recommendation problem
because users have a strong preference on the diversity between past and
future clicked news.

Many other common deep models, such as CNN (Zhang et al., 2021a), self-
attention (Wu et al., 2019e) and co-attention (Meng et al., 2021), have also
been applied in user modeling. For example, WE3CN (Khattar et al., 2018)
learns representations of users from the 3D representation tensors of their
clicked news using a 3D CNN model. SFI (Meng et al., 2021) further introduces
a hard selection mechanism to reduce the computational cost in 3D CNN-based
user modeling. NRMS (Wu et al., 2019e) learns contextual news representations
by using a news-level multi-head self-attention network, and uses an additive
attention network to form the user representation. This method is also adopted
by many methods like FairRec (Wu et al., 2021i), IMRec (Xun et al., 2021) and
SentiRec (Wu et al., 2020d), and the variant that uses “CLS” token
representation of Transformer is used by (Zhang et al., 2021c). FIM (Wang et
al., 2020c) uses a fine-grained interest modeling method that can capture the
word-level relatedness between news with a 3D CNN model. UniRec (Wu et al.,
2021g) learns the user embedding for news ranking with the NRMS (Wu et al.,
2019e) model, and then uses this embedding as the attention query to select a
set of basis interest embeddings to aggregate them into a user embedding for
news recall. KIM (Qi et al., 2021a) uses a user-news co-encoder that models
the interactions between candidate news and clicked news to collaboratively
learn a candidate-aware user interest representation and a user-aware
candidate news representation. PP-Rec (Qi et al., 2021b) uses a popularity-
aware user modeling method that first uses self-attention to model the
contexts of user behaviors and then uses a content-popularity joint attention
network that selects clicked news according to their content and popularity
for user interest modeling. RMBERT (Jia et al., 2021) uses a reasoning memory
network (Fu et al., 2020) to capture the sophisticated interactions between
user behaviors and candidate news in user interest modeling. These methods can
effectively capture the relations of different user behaviors to enhance user
modeling.

In recent years, graph neural networks have also been applied to model the
contexts of user behaviors by capturing their high-order relations (Mao et
al., 2021). For example, CAGE (Sheu and Li, 2020) first uses a GCN model to
capture the relations between different behaviors within a news session to
refine the behavior representations, and then uses a GRU network to build user
representations. User-as-Graph (Wu et al., 2021b) is probably the first work
in news recommendation that represents each user with a personalized
heterogeneous graph constructed from click behaviors, where the user modeling
task is modeled as a graph pooling problem. It uses a heterogeneous graph
pooling method named HG-Pool to iteratively summarize the personalized
heterogeneous graph for learning user interest representations. EEG (Zhang et
al., 2021d) models each user as an entity graph. It first uses a graph neural
network to learn hidden entity representations, and then uses an attention
network to aggregate them into an entity-based user representation. KOPRA
(Tian et al., 2021) also models users as entity graphs and uses recurrent
graph convolution to process the entity graphs. It models both long-term and
short-term user interests with the entire graph and the subgraph inferred from
recently clicked news, respectively. In addition, it introduces an entity
neighbor pruning technique to select entity neighbors according to user
interests. CNE-SUE (Mao et al., 2021) applies a GCN to different subgraphs of
an entire user behavior graph to learn different interest representations for
different behavior clusters. It further employs an intra-cluster attention
mechanism to pool node representations and uses an intra-cluster attention
mechanism to aggregate cluster representations. These methods can usually
capture the rich high-order relatedness between users’ click behaviors to
discover latent user interest.

In addition to click behaviors, a few methods also consider the ID information
of users (Zhang et al., 2018, 2019a). For example, NPA (Wu et al., 2019c) uses
a news-level personalized attention network to select important news according
to user characteristics, where the embeddings of user IDs are used to generate
the attention queries. LSTUR (An et al., 2019) learns short-term user interest
embeddings by a GRU network, and models long-term user interests by the
embeddings of user IDs. To fuse the two kinds of user representations, LSTUR
explores two methods, i.e., concatenating two vectors together, or using the
long-term user interest embedding to initialize the hidden state of the GRU
network. This framework is further used by CUPMAR (Tran et al., 2021) and KG-
LSTUP (Sun et al., 2021b). These methods can usually better serve active users
with rich behaviors to tune their ID embeddings. However, they have some
difficulties in handling cold-start users without well-tuned user embeddings.

All the aforementioned methods mainly rely on the information of users’ click
behaviors. However, click behaviors are very noisy and may not necessarily
indicate user interest, and it is difficult to comprehensively and accurately
infer user interest from click feedback only. Thus, a few methods study
incorporating other kinds of user information to enhance user interest
modeling (Zhang et al., 2019b). One major direction is adding context features
to enhance user modeling. For example, CHAMELEON (Gabriel De Souza et al.,
2019; de Souza Pereira Moreira et al., 2018) uses several user context
features like time, device, location and referrer. It uses a UGRNN network to
learn representations of users in a session, and the click score is evaluated
by the cosine similarity between user and candidate news representations. The
context features used in these methods can provide rich information for
inferring users’ current preferences to improve subsequent news
recommendation.

Another main direction is incorporating various kinds of user behaviors (Ma et
al., 2021). For example, NRHUB (Wu et al., 2019d) considers heterogeneous user
behaviors, including news clicks, search queries, and browsed webpages. It
incorporates different kinds of user behaviors as different views of users by
learning a user embedding from each kind of user behaviors separately, where a
combination of CNN and attention network is used to learn behavior
representations and a behavior attention network is used to learn a user
embedding by selecting important user behaviors. The user embeddings from
different views are aggregated into a unified one via a view attention
network. The effectiveness of webpage browsing behaviors in user modeling for
news recommendation is also studied by WG4Rec (Shi et al., 2021). CPRS (Wu et
al., 2020e) considers users’ click and reading behaviors in user modeling. It
models the click preference of users from the titles of clicked news, and
models their reading satisfaction from the body of clicked news as well as the
personalized reading speed metric derived from dwell time and body length.
NRNF (Wu et al., 2020b) uses a dwell time threshold to divide click news into
positive ones and negative ones. It uses separate Transformers and attention
networks to learn positive and negative user interest representations. FeedRec
(Wu et al., 2021d) uses various kinds of user feedback including click,
nonclick, finish, quick close, share and dislike to model user interest. It
uses a heterogeneous Transformer to model the relatedness between all kinds of
feedback and uses different homogeneous Transformers to model the interactions
between the same kind of feedback. In addition, it uses a strong-to-weak
attention network that uses the representations of strong feedback to distill
real positive and negative user interest information from weak feedback. These
methods can usually infer user interests more accurately by mining
complementary information encoded in multiple kinds of user behaviors.

There are also several methods that learn user representations on graphs that
involve the collaborative information of users and news. For example, IGNN
(Qian et al., 2019) learns content-based user representations using the
average embedding of clicked news, and learns graph-based user representations
from the user-news graph via a graph neural network. The content-based user
representation is concatenated with graph-based user representation to form a
unified one. GNewsRec (Hu et al., 2020a) uses the same architecture with DAN
to learn short-term user representations, and uses a two-layer graph neural
network (GNN) to learn long-term user representations from a heterogeneous
user-news-topic graph. Both short-term and long-term user representations are
concatenated to build a unified user representation. GERL (Ge et al., 2020)
uses multi-head self-attention and additive attention networks to form
content-based user representations from the click history. In addition, it
uses a graph attention network to learn graph-based representations of users
by capturing high-order information on the user-news graph, which are further
combined with the content-based user representations. MVL (Santosh et al.,
2020) uses attention networks to learn user interest representations in a
content view, and uses a graph attention network to model user interest from
the user-news graph in a graph view. GNUD (Hu et al., 2020b) uses a
disentangled graph convolution network to learn user representations from the
user-news graph. These methods can exploit the high-order information on
graphs to enhance user modeling. GBAN (Ma et al., 2021) combines user
embeddings learned by an LSTM and heterogeneous graph embeddings. It further
introduces subgraph core and coritivity scores that measure the importance of
a target user-news pair in the subgraph to enhance user representations. These
methods can take the advantage of high-order interaction information between
user and news as well as the associated meta features. However, it is
challenging for them to accurately represent new users that do not participate
in the model training.

Table 5. Comparison of different methods on user modeling.

Method | Year | Information Used | Model
---|---|---|---
EBNR (Okura et al., 2017) | 2017 | News Click | GRU
RA-DSSM (Kumar et al., 2017b) | 2017 | News Click | Bi-LSTM+Attention
Khattar et al. (Kumar et al., 2017c) | 2017 | News Click | Exponential-decayed Average
3-D-CNN (Kumar et al., 2017a) | 2017 | News Click | Word2vec
Park et al. (Park et al., 2017) | 2017 | News Click | LSTM
WE3CN (Khattar et al., 2018) | 2018 | News Click | 3-D CNN
DKN (Wang et al., 2018) | 2018 | News Click | Candidate-Aware Attention
Gao et al. (Gao et al., 2018) | 2018 | News Click | Candidate-Aware+Multi-Head Attention
Saskr (Chu et al., 2019) | 2019 | News Click | Self-Attention+Candidate-Aware Attention
NAML (Wu et al., 2019b) | 2019 | News Click | Attention
TANR (Wu et al., 2019a) | 2019 | News Click | Attention
NRMS (Wu et al., 2019e) | 2019 | News Click | Self-Attention+Attention
Liu et al. (Liu et al., 2019) | 2019 | News Click | Time-decayed Average
DAN (Zhu et al., 2019) | 2019 | News Click | LSTM+Self-Attention+Candidate-Aware Attention
KRED (Liu et al., 2020) | 2020 | News Click | Attention
TEKGR (Lee et al., 2020) | 2020 | News Click | Candidate-Aware Attention
FIM (Wang et al., 2020c) | 2020 | News Click | 3-D CNN
FedRec (Qi et al., 2020) | 2020 | News Click | Self-Attention+Attention+GRU
SentiRec (Wu et al., 2020d) | 2020 | News Click | Transformer+Attention
CAGE (Sheu and Li, 2020) | 2020 | News Click | GCN+GRU
PLM-NR (Wu et al., 2021c) | 2021 | News Click | Attention
UniRec (Wu et al., 2021g) | 2021 | News Click | Self-Attention+Attention+Basis User Embedding
FairRec (Wu et al., 2021i) | 2021 | News Click | Transformer+Attention
MM-Rec (Wu et al., 2021f) | 2021 | News Click | Crossmodal Candidate-Aware Attention
IMRec (Xun et al., 2021) | 2021 | News Click | Self-Attention+Attention
HieRec (Qi et al., 2021d) | 2021 | News Click | Hierarchical Attention
KIM (Qi et al., 2021a) | 2021 | News Click | User-News Co-Encoder
PP-Rec (Qi et al., 2021b) | 2021 | News Click | Content-Popularity Joint Attention Network
CTX (Cho et al., 2021) | 2021 | News Click | Add on existing methods
DCAN (Meng et al., 2021) | 2021 | News Click+Click Time+Context Features | LSTM+Co-Attention
DebiasRec (Yi et al., 2021a) | 2021 | News Click | Content Attention+Bias Attention
User-as-Graph (Wu et al., 2021b) | 2021 | News Click | Heterogeneous Graph Pooling
AMM (Zhang et al., 2021b) | 2021 | News Click | Attention
RMBERT (Jia et al., 2021) | 2021 | News Click | Reasoning Memory Network
UNBERT (Zhang et al., 2021c) | 2021 | News Click | Transformer
EEG (Zhang et al., 2021d) | 2021 | News Click | GNN+Attention
KOPRA (Tian et al., 2021) | 2021 | News Click | Recurrent Graph Convolution
SFI (Zhang et al., 2021a) | 2021 | News Click | Hard Similarity Selection+3-D CNN
TempRec (Wu et al., 2021e) | 2021 | News Click | Temporal Diversity-aware Transformer
D2NN (Raza and Ding, 2021a) | 2021 | News Click | Embedding Sum+LSTM+Attention
EENR (Han et al., 2021) | 2021 | News Click | Attention+Event Type/Category Distribution
CNE-SUE (Mao et al., 2021) | 2021 | News Click | GCN+Attention
DeepJoNN (Zhang et al., 2018) | 2018 | News Click+User ID | LSTM
NPA (Wu et al., 2019c) | 2019 | News Click+User ID | Personalized Attention
LSTUR (An et al., 2019) | 2019 | News Click+User ID | GRU+ID Embedding
DNA (Zhang et al., 2019a) | 2019 | News Click+User ID+Time Interval | Attention+CNN
CUPMAR (Tran et al., 2021) | 2021 | News Click+User ID | GRU+ID Embedding
KG-LSTUP (Sun et al., 2021b) | 2021 | News Click+User ID | GRU+ID Embedding
CHAMELEON (Gabriel De Souza et al., 2019) | 2019 | News Click+Context Features | UGRNN
DAINN (Zhang et al., 2019b) | 2019 | News Click+Time+Location | Attention
NRHUB (Wu et al., 2019d) | 2019 | News Click+Query+Webpage | Attention
CPRS (Wu et al., 2020e) | 2020 | News Click+News Reading | Attention+Content-Satisfaction Attention
NRNF (Wu et al., 2020b) | 2020 | Positive News Click+Negative News Click | Transformer+Attention
FeedRec (Wu et al., 2021d) | 2021 | News Click+Nonclick+Finish+Quick Close+Share+Dislike | Transformer+Strong-to-Weak Attention
WG4Rec (Shi et al., 2021) | 2021 | News Click+Webpage | GRU+Attention
IGNN (Qian et al., 2019) | 2019 | News Click+User-News Graph | GNN
INNR (Ren et al., 2019) | 2019 | Heterogeneous Graph | Node2vec
GNewsRec (Hu et al., 2020a) | 2020 | News Click+Heterogeneous Graph | LSTM+Attention+GNN
GERL (Ge et al., 2020) | 2020 | News Click+User-News Graph | Self-Attention+GAT
MVL (Santosh et al., 2020) | 2020 | News Click+User-News Graph | Self-Attention+GAT
GNUD (Hu et al., 2020b) | 2020 | User-News Graph | Disentangled GCN
AGNN (Ji et al., 2021a) | 2021 | News Click+Heterogeneous Graph | LSTM Variant (Gers et al., 2000)+GNN+Self-Attention+Attention
TSHGNN (Ji et al., 2021b) | 2021 | News Click+Heterogeneous Graph | LSTM Variant+GNN+Self-Attention+Attention
GBAN (Ma et al., 2021) | 2021 | Click+Nonclick+Like+Follow+Comment+Share+Heterogeneous Graph | Core and DeepWalk+Coritivity Scores+LSTM+Attention
|  |  |

We summarize the user information and user modeling techniques used in these
deep learning-based methods in Table 5. We then provide several discussions on
the user modeling methods introduced in this section.

###  4.3. Discussions on User Modeling

####  4.3.1. Feature-based User Modeling

Most feature-based methods construct user profiles based on the collections of
features extracted from the clicked news. Besides the news information, some
methods leverage additional user features to facilitate user modeling. For
example, the demographics of users (e.g., age, gender and profession) are used
in several methods, since users with different demographics usually have
different preferences on news. The location of users can be used to identify
the news related to the user’s neighborhood, and the access patterns of users
can also help understand the news click behaviors of users. In addition, many
methods use the tags or keywords of users to indicate user interest, and
cluster users based on their characteristics. In this way, the recommender
system can more effectively recommend news according to users’ interest in
different topics. Moreover, several methods incorporate user behaviors on
other platforms, such as social media, search engines and e-commerce
platforms. These behaviors can not only facilitate user interest modeling, but
also has the potential to mitigate the problem of cold-start on the news
platform if user data can be successfully aligned. However, feature-based user
modeling methods usually require massive expertise for feature design and
validation, and may not be optimal for representing user interests.

####  4.3.2. Deep Learning-based User Modeling

Deep learning-based user modeling methods usually aim to learn user
representations from user behaviors without feature engineering. Many of them
infer user interests merely from click behaviors, because click behaviors are
implicit indications of users interest in news. However, click behaviors are
usually noisy and they do not necessarily indicate real user interests. Thus,
many methods consider other kinds of information in user modeling. For
example, some methods such as NPA and LSTUR incorporate the IDs of users to
better capture users’ personal interest. CHAMELEON and DAINN consider the
context features of users such as devices and user locations. CPRS, FeedRec
and GBAN incorporate multiple kinds of user feedback on the news platform to
consider user engagement information in user interest modeling. GERL and
GNewsRec can exploit the high-order information on graphs to encode user
representations. However, it is still difficult for these methods to
accurately infer user interests when user behaviors on the news platforms are
sparse. There are only two methods, i.e., NRHUB and WG4Rec, that consider
users’ behaviors on multiple platforms, which can still model users accurately
even user behaviors on the news platform are sparse. However, there may exist
some difficulties in linking user data on different platforms due to privacy
reasons.

According to the summarization in Table 3, we can see that the model
architectures used for user representation learning are diverse. Some methods
utilize recurrent neural networks to capture the relatedness of news clicked
by users, such as EBNR, DAN and CHAMELEON. With the great success of
Transformer models, many methods also use self-attention or Transformer
networks to model the global contexts of user behaviors. However, these
sequential models cannot effectively model the high-order relations between
user behaviors, which can provide useful contexts for user interest
understanding. Instead of modeling user behaviors as a sequence, several
methods like User-as-Graph model each user as a personalized graph, where the
high-order relations between behaviors can be fully modeled. In addition,
several works such as GERL and GNUD use graph neural networks to capture the
high-order interactions between users and news on the global user-news graphs,
which can also help better understand user interest by incorporating
collaborative information. However, the computational cost of these graph-
based architectures is usually much heavier than sequential models, and
collaborative signals are usually not available for cold-start users and news.

To select clicked news that is informative for inferring user interest,
attention mechanisms are widely used by many methods. In some works such as
NAML and KRED, the attention query is a global parameter vector, which is
invariant with respect to different users. In the NPA method, the attention
query is generated by the embedding of user ID, which can achieve personalized
news selection. Both kinds of attention mechanisms are efficient in the online
test phase because user representations can be prepared in advance (Wu et al.,
2019c). However, the relatedness between candidate news and clicked news
cannot be fully modeled, which may not be optimal in modeling user interests
in a specific candidate news. Another kind of attention mechanism, i.e.,
candidate-aware attention, is also widely used by many methods such as DKN,
DAN and KIM. In candidate-aware attention networks, the representation of
candidate news is used as the attention query, and user representations can be
dynamically constructed based on candidate news. However, they need to
memorize the representations of all clicked news in the test phase, which may
lead to some sacrifice in efficiency.

Some methods study modeling multiple types of user interests. For example,
LSTUR, GNewsRec and FedRec consider both long-term and short-term interests of
users to better capture their interest dynamics. HieRec models the
hierarchical structure of user interests, which can capture the user interests
in different granularities. These methods can improve user interest
understanding of user interests by taking different kinds of user interest
into consideration. However, user interests are diverse and evolutional, which
are still difficult to be comprehensively and accurately modeled by these
methods.

####  4.3.3. Differences to User Modeling in General Recommendation

The user modeling techniques used for personalized news recommendation have
close relations to the user modeling methods in general recommendation
scenarios such as e-commerce (Zhou et al., 2018) and movie recommendation
(Diao et al., 2014). For example, the core neural architectures such as RNN,
CNN, self-attention and graph neural networks, are also widely used for
sequential recommendation. In addition, several useful user modeling paradigms
such as long short-term user interest modeling are also popular in other
recommendation fields. However, by scrutinizing recent literature, we find
there are several unique characteristics of user modeling in personalized news
recommendation:

(1) Short news lifecycles. Different from the common e-commerce recommendation
scenarios where items can be actively interacted with for months or even years
(Zhou et al., 2020), most news articles have very short lifecycles (i.e., a
few days). Thus, in news recommendation it is important to take this unique
characteristic into consideration when designing user modeling algorithms. For
example, in GNN-based methods for general recommendation, item nodes can be
simply represented by ID embeddings. However, in GNN-based news recommender,
it is better to learn embeddings of news nodes from news content to handle
uncovered news in user modeling (Hu et al., 2020a). In addition, the quick
vanishment of old news de facto limits the exploitation of collaborative
signals in user modeling due to the large fraction of cold news in the
inference stage.

(2) Fine-grained candidate-aware user modeling. In many recommendation tasks,
items are mainly represented by their overall embeddings (Sun et al., 2019),
and modeling feature interactions is important for candidate-aware user
modeling (Zhou et al., 2018). By contrast, news articles have rich content and
context information, and the interactions between user behaviors and candidate
news can be modeled in a more fine-grained way (e.g., word-level
interactions). Capturing the fine-grained relevance between user behaviors and
candidate news is very important for understanding user interest in a specific
candidate news article. Thus, fine-grained candidate-aware user modeling is a
core technique used in many recent news recommendation methods.

(3) User modeling as document modeling. Different from many recommendation
scenarios where user behaviors are not associated with sufficient textual
information (Zhou et al., 2019), in news recommendation user behaviors are
usually clicked news that contain rich texts. Thus, the user modeling problem
in news recommendation can be formulated as a document modeling problem, where
the texts of clicked news are embedded in user “documents”. Several recent
methods follow this setting and employ strong pre-trained language models to
empower user modeling (Zhang et al., 2021c; Jia et al., 2021). The success of
these PLM-based user modeling techniques indicates that there may not be a
huge barrier between NLP and user modeling for news recommendation, which is a
unique characteristic of the news recommendation field.

(4) Potential strong temporal diversity preference. Different from general
recommendation scenarios where users may prefer to click very similar items,
in news recommendation users tend to click news that are somewhat different
from the previously clicked ones (Abdollahpouri et al., 2021) (i.e.,
preference on serendipity). As pointed by a recent study (Wu et al., 2021e),
it may not be very suitable to model news recommendation as a standard
sequential recommendation task, and it is important to consider such temporal
diversity preference in user modeling. The results show that many sequential
models such as RNN (Hidasi et al., 2016) and casual self-attention (Kang and
McAuley, 2018) are inferior to standard self-attention that focus more on
global context rather than sequential dependency. Further study on this unique
phenomenon is needed to better understand user modeling mechanism in news
recommendation.

In summary, by reviewing user modeling techniques used in existing news
recommendation methods, we argue that user modeling is also remained
challenging due to many reasons, such as the noise and sparsity of user
behaviors, the diverse and dynamic characteristics of user interests, and the
difficulties in modeling user interests in a specific candidate news
effectively and efficiently.

##  5\. Personalized Ranking

On the basis of news and user modeling, news ranking aims to rank candidate
news for personalized display according to users’ personal interest. Common
news ranking techniques can be divided into two categories, i.e., relevance-
based and reinforcement learning-based. We introduce them in the following
sections.

###  5.1. Relevance-based Personalized Ranking

Relevance-based news ranking methods usually rank candidate news with user
interests based on their personalized relevance. In these methods, how to
accurately measure the relevance between candidate news and user interest is a
core problem. Many methods directly evaluate the user-news relevance based on
the similarities of their final representations. For instance, Goossen et al.
(Goossen et al., 2011) computed the cosine similarities between the CF-IDF
feature vectors of user and news to measure their relevance. Garcin et al.
(Garcin et al., 2012) used the similarities between the news topic vectors and
the user topic vector to evaluate their relevance. Okura et al. (Okura et al.,
2017) used the inner product between news and user representations to predict
the relevance scores. DFM (Lian et al., 2018) uses an inception module that
combines neural networks with different depths to compute the relevance scores
from news and user features. These methods usually employ two-tower
architectures, which enable efficient inference by computing news and user
features in advance. However, user interests are usually diverse, and
candidate news may only match the user interests indicated by a part of the
clicked news. These methods cannot fully consider the relatedness between
candidate news and clicked news, and the matching between candidate news and
user interest may not be very accurate.

A few methods use fine-grained interest matching techniques to better model
the relevance between users’ interest and candidate news. For example, FIM
(Wang et al., 2020c) first multiplies together the word representations of
candidate news and clicked news, and then uses a matching module with 3-D CNN
networks to compute relevance scores by capturing the fine-grained relatedness
between candidate news and clicked news. KIM (Qi et al., 2021a) first uses a
knowledge-aware news co-encoder to model the relatedness between words and
entities in candidate news and clicked news, and further uses a user-news co-
encoder to further help model the interactions between clicked news and
candidate news for better relevance modeling. HieRec (Qi et al., 2021d) has a
hierarchical interest matching mechanism that matches candidate news with the
fine-grained subtopic-level user interest, the coarse-grained topic-level user
interest and the overall user interest. AMM (Zhang et al., 2021b) uses a
multi-field matching scheme to model the interactions between each pair of
views of a clicked news and a candidate news. These single-tower methods can
more accurately evaluate the relevance between candidate news and user
interest by modeling their fine-grained and multi-grained relatedness, which
can help generate news ranking results that better target user interest.
However, these methods usually have much larger computational costs in the
inference stage than coarse-grained interest matching, which may hinder their
application in some low-latency or low-resource scenarios.

In most methods, candidate news with higher relevance to user interest will
gain higher ranks. However, these methods may tend to recommend news that are
similar to those previously clicked by users, which is also called the “filter
bubble” problem. Thus, some news ranking methods explore to recommend news
that are somewhat different from previously clicked ones to introduce
diversity and serendipity (Abdollahpouri et al., 2021). For example,
Newsjunkie (Gabrilovich et al., 2004) is a system that ranks news articles
based on their novelty in the context of the news that users previously
clicked. SCENE (Li et al., 2011b) first ranks news articles based on their
relevance to user interests, and then refines the ranking list based on news
popularity and recency to form the final recommendation list. Different from
the methods that are solely based on the relevance between candidate news and
user interests, these methods have the potential to provide more diverse
recommendations.

###  5.2. Reinforcement Learning-based Personalized Ranking

Different from relevance-based ranking methods that mainly aim to optimize the
objectives (e.g., clicks) on current candidate news articles, reinforcement
learning-based ranking methods usually aim to optimize the total reward in a
long term (Zheng et al., 2018; Song et al., 2021). A representative
reinforcement learning-based approach to personalized news recommendation is
LinUCB (Li et al., 2010a), which models the problem of personalized news
recommendation as a contextual bandit problem. In this method, LinUCB computes
the payoff by a hybrid linear model, which means that some parameters are
shared by all arms, while the others are not. LinUCB can outperform context-
free bandit methods such as $\epsilon$-greedy and Upper Confidence Bound
(UCB), and it is computationally efficient because the block parameters in
LinUCB have fixed dimensions and can be incrementally updated (Li et al.,
2010a). It is also latterly evaluated by (Li et al., 2011a) in an unbiased
manner by estimating the per-trial payoff with log data directly rather than a
simulator. In the CLEF NewsREEL 2017 challenge, Liang et al. (Liang et al.,
2017) also developed a system based on LinUCB. The LinUCB model is used to
help choose the appropriate recommender from a pool of recommendation
algorithms based on user and news features. Deep reinforcement learning is
also explored in news recommendation (Zheng et al., 2018; Islambouli et al.,
2021; Song et al., 2021). For example, DRN (Zheng et al., 2018) uses a Deep
Q-Network (DQN) to estimate the policy reward, which is a weighted summation
of click labels and the activeness of users that is computed based on their
return time after recommendations. In addition, DRN applies the Dueling Bandit
Gradient Descent (Yue and Joachims, 2009) algorithm to eliminate the
recommendation performance decline brought by classical exploration methods
such as $\epsilon$-greedy and UCB. Different from relevance-based ranking
methods, reinforcement learning-based ranking methods have the ability of
exploration, which can increase the diversity of recommendation results and
further discover potential user interests.

###  5.3. Discussions on Personalized Ranking

In this section we provide some discussions on the news ranking methods in
existing personalized news recommender systems. Relevance-based news ranking
methods mainly need to accurately evaluate the relevance between candidate
news and user interest for subsequent news ranking. Many methods model their
overall relevance by evaluating the relevance between the unified
representations of user interest and candidate news. However, candidate news
usually can only match part of user interests, and directly match the overall
user interest with candidate news may be suboptimal. A few methods explore to
evaluate the relevance between user interest and candidate news in a fine-
grained way by modeling the relatedness between candidate news and clicked
news, which can improve the accuracy of relevance modeling for news ranking.
However, these methods are much more time-consuming because the
representations of users are dependent on candidate news and cannot be
computed in advance. Moreover, pure relevance-based interest matching methods
may tend to recommend news that are similar to previously clicked news, which
is not beneficial for users to receive diverse news information. Thus, a few
works explore to adjust the news ranking strategy by incorporating other
factors such as news novelty, popularity and recency, which have the potential
to make more diverse news recommendations and mitigate the filter bubble
problem in news recommender systems.

In relevance-based news ranking methods, candidate news is usually greedily
matched with users, i.e., choosing the news in each impression that mostly
satisfy the ranking policy on the current candidate news list. However, it may
not be optimal in improving long-term user experience. In reinforcement
learning-based methods, the ranking algorithm aims to find the optimal ranking
policy to maximize the long-term reward. Thus, RL-based news ranking methods
may be more suitable for exploring potential user interest and improving long-
term user experience and engagement, while it may have some sacrifice in
short-term news CTRs.

In summary, news ranking in news recommendation also faces many challenges,
including how to accurately and efficiently evaluate the relevance between
candidate news and user interest indicated by user behaviors, how to mitigate
the “filter bubble” problem in news recommender systems, and how to explore
potential user interests without hurting user experience.

##  6\. Model Training

Many personalized news recommendation methods exploit machine learning models
for news modeling, user modeling and interest matching. Training these models
is a necessary step in building an accurate news recommender system. In this
section, we review the techniques used for model training in news
recommendation.

###  6.1. Training Methods

In a few methods based on collaborative filtering, the news recommendation
task is formulated as a rating prediction problem, i.e., predicting the
ratings that users give to news (Ji et al., 2016). To learn their models, they
usually use loss functions such as the mean squared error (MSE) computed
between the predicted ratings and the gold ratings, which are further used to
optimize the model (Claypool et al., 1999). However, explicit user feedback
like rating is usually very sparse, which may be insufficient to train an
accurate recommendation model.

Since implicit feedback such as click is abundant, most methods use the click
feedback of users as the prediction target. They formulate the news
recommendation task as a click prediction task. Some methods simply classify
whether a candidate news will be clicked by a target user (Fortuna et al.,
2010; Gershman et al., 2011; Wang et al., 2018). However, these methods cannot
exploit the relatedness between clicked and nonclicked samples. Thus, a few
methods use contrastive training techniques to maximize the margin between the
predicted click scores of clicked and nonclicked news. For example, PP-Rec (Qi
et al., 2021b) uses the Bayesian Personalized Ranking (BPR) loss for model
training by comparing each clicked sample with an nonclicked one. However, the
BPR loss can only exploit a small part of nonclicked samples. NPA (Wu et al.,
2019c) uses the InfoNCE (Oord et al., 2018) loss for model training. For each
clicked sample (regarded as a positive sample), it randomly samples a certain
number of nonclicked ones (regarded as negative samples) and jointly predicts
their click scores. These click scores are further normalized by the softmax
function to compute the posterior click probabilities, and the model aims to
maximize the negative log-likelihood of the posterior click probability of
positive samples. In this way, the model can exploit the information of more
negative samples.

Besides click feedback, a few methods also consider other kinds of feedback to
construct training tasks. For example, CPRS (Wu et al., 2020e) trains the
recommendation model collaboratively in the click prediction task and an
additional reading satisfaction prediction task, which aims to infer the
personalized reading speed based on user interest and news body. FeedRec (Wu
et al., 2021d) trains the model in three tasks, including click prediction,
dwell time prediction and finish prediction. GBAN (Ma et al., 2021) models the
recommendation task as a future behavior classification problem to predict the
behavior type of a user on a specific candidate (i.e., click, nonclick, like,
follow, comment, and share).222We argue that this setting is somewhat
difficult to be implemented in real-world news recommender systems because it
is not clear how to rank news accordingly and users may also have multiple
types of behaviors on the same news. These methods can encourage the model to
optimize not only CTR but also user engagement, which can help learn
engagement-aware news recommendation models.

There are several methods that use additional news information to design
auxiliary training tasks. For example, EBNR (Okura et al., 2017) uses
autoencoder to learn news representations and it uses another weak supervision
task by encouraging the embeddings of news in the same topic to be similar
than the embeddings of news in different topics. TANR (Wu et al., 2019a) uses
an auxiliary news topic prediction task to help learn topic-aware news
representations. SentiRec (Wu et al., 2020d) uses a news sentiment orientation
score prediction task to learn sentiment-bearing news representations. KRED
(Liu et al., 2020) trains the model in various tasks including item
recommendation, item-to-item recommendation, category classification,
popularity prediction and local news detection. These methods can also
effectively encode additional information into the recommendation model
without taking it as the input. However, it is usually non-trivial to balance
the main recommendation task and the auxiliary tasks.

###  6.2. Training Environment

Existing researches mainly focus on the model training methods while ignoring
the implementation environment of model training, which is in fact important
in developing real-world news recommender systems. In many existing methods,
the news recommendation models are offline trained on centrally stored data
with centralized computing resources (Wu et al., 2020a). This model training
paradigm can help quick development of news recommender systems, but it also
has several main drawbacks. First, user behavior data for model training is
usually abundant and many recent news recommendation models are in large size
(Wu et al., 2021c), which require a large amount of computing resource to
train accurate models. Although some recent works like (Wu et al., 2021c)
explore to train models in parallel on multiple GPUs, it is still insufficient
to train huge models. Thus, distributed model learning with proper
acceleration methods like data rearrangement and cache mechanisms may be
required in industrial practice (Xiao et al., 2021). Second, the model learned
on offline data only may also have some mismatches with the characteristics of
recommendation scenarios (Zheng et al., 2018). Moreover, the distribution of
user interest and news topics may also evolve, and it is shown in previous
research that the performance of offline trained models may decline with time
(Wu et al., 2019c). Thus, instead of re-training models periodically, online
model training on streaming data is needed. Third, most existing news
recommendation methods are trained on centrally stored user data, which may
have some privacy risks because user data usually contains private user
information. Several recent works like (Qi et al., 2020, 2021c; Yi et al.,
2021b) explore to train news recommendation models based on decentralized data
with federated learning techniques, which can better protect user privacy in
model training.

###  6.3. Discussions on Model Training

Next, we provide some discussions on the model training techniques used in
news recommendation methods. In some CF-based methods, news recommendation is
modeled as a regression task where the ratings given by users are regarded as
prediction targets. However, on news platforms explicit user feedback such as
rating is usually scarce, which poses great challenges to model training.
Therefore, most methods adopt implicit feedback to construct training tasks.
Click feedback is one of the most widely used signals for model training
because it can implicitly indicate user interests in news and help the model
optimize the CTR of recommendation results. However, click signals also have
some gaps with the real user interests (Yi et al., 2014), and increasing CTR
only may lead to recommending clickbait news to users, which is actually
harmful to user experience. Thus, a few methods incorporating other user
engagement signals such as dwell time and finish into model training, which
can help learn user engagement-aware recommendation model to improve user
experience. Besides user feedback, some methods also consider using additional
news information as auxiliary prediction objectives. By jointly training the
model in both recommendation task and auxiliary tasks, the model can be aware
of the additional news information. Since these methods do not take the
additional features as the input, they can handle the scenarios where the
additional features are unavailable. However, in multi-task learning based
methods, it is difficult to choose the proper coefficients for weighting the
loss functions of different tasks, and these coefficients may also be
sensitive to the dataset characteristics.

Another important problem in model training is designing effective strategies
for constructing labeled training samples. In most methods the negative
samples are randomly drawn from the entire news set or the impression list (Wu
et al., 2021g), which are further packed with the positive samples. However,
researchers have found that randomly selected negative samples may be too easy
for the model to distinguish, which is not beneficial for learning
discriminative recommendation models (Li et al., 2019). It is also an
interesting problem to study the influence of the number of negative samples
on model training (Wu et al., 2021a).

Besides, the environment for news recommendation model training is a less
studied but important problem. Most researches are offline conducted by
learning models on centralized data with centralized computing resources. As
discussed in the previous section, this model training environment may pose
many potential challenges like the limitation of centralized computing
resources, the gaps between offline data and online applications, and the
privacy concerns and risks of centralized model training, which need to be
extensively studied in the future.

In summary, model training is critical for news recommendation while it still
has much room for improvement, such as designing more effective training
tasks, choosing more representative training samples, adaptively tuning the
loss coefficients for multi-task learning, and building more effective,
efficient and privacy-preserving environment for news recommendation model
training.

####  6.3.1. A Bird’s-eye View on Recent Approaches

To help readers better understand the details of recent news recommendation
methods in terms of their news modeling, user modeling, ranking, and model
training techniques, we illustrate a joint table that summarizes their details
in these aspects. Due to the limitation of page sizes, we do not include it in
the main content, and readers can refer to it in a public repository
(https://github.com/wuch15/News-Rec-Survey).

##  7\. Evaluation Metrics

There are many metrics to quantitatively evaluate the performance of news
recommender systems. Most metrics aim to measure the recommendation
performance in terms of the ranking relevance. For methods that regard the
task of news recommendation as a classification problem, the Area Under Curve
(AUC) score is a widely used metric, which is formulated as follows:

(1) $\rm{AUC}=\frac{|\\{(i,j)|Rank(p_{i})<Rank(n_{j}))\\}|}{N_{p}N_{n}},$

where $N_{p}$ and $N_{n}$ are the numbers of positive and negative samples,
respectively. $p_{i}$ is the predicted score of the $i$-th positive sample and
$n_{j}$ is the score of the $j$-th negative sample. Another set of popular
metrics are precision, recall and F1 scores, which are computed as:

(2) $\displaystyle\rm{Precision}$ $\displaystyle=\frac{TP}{TP+FP},$ (3)
$\displaystyle\rm{Recall}$ $\displaystyle=\frac{TP}{TP+FN},$ (4)
$\displaystyle\rm{F1}$
$\displaystyle=\frac{2*\rm{Precision}*\rm{Recall}}{\rm{Precision}+\rm{Recall}},$

where TP, FP and FN respectively denote true positive, false positive and
false negative.