Table 16: Comprehensive Overview Recommender System Models for FATE across Various Metrics and Use Cases. This table details each model’s Input features, Year of Publication, and Characteristics such as Scalability, Interpretability, Efficiency, and Reproducibility (rated as High, Medium, or Low). It also lists the Dataset Used, Evaluation Metrics, Model Accuracy, Learning Task, and Application Field. Model |  Year |  Input Data |  Scalability, Interpretability, Efficiency, Reproducibility |  Dataset |  Evaluation Metrics |  Model Accuracy |  Application
---|---|---|---|---|---|---|---
Antidote Data Adding[402] |  2019 |  user-item interaction |  \- , -, High, Low |  MovieLens |  Polarization, unfairness |  None |  e-commerce
Beyond Parity [403] |  2017 |  user-item interaction |  -, -, High, Low |  MovieLens |  Error, unfairness |  0.887, 0.010 |  e-commerce
IERS[404] |  2018 |  user-item interaction |  -, -, High, Low |  MovieLens, Flixter, Sushi |  MAE, degree of independence |  Movielens: 0.7/ 0.01, Flixter: 0.65/ 0.01, Sushi: 0.92/ 0.05 |  e-commerce
Fairness-aware TR[405] |  2018 |  user-item interaction |  \- , - , -, Low |  MovieLens, Twitter |  Precision@15, Recall@15 |  Movielens: 0.032/ 0.08, Twitter: 0.03298, 0.0687 |  e-commerce, Social Networks
Fairness Pairwise Comparisons[406] |  2019 |  user-item interaction |  -, - , -, Low |  Synthetic data |  Overall Pairwise accuracy, intra-group Pairwise Accuracy |  35.6%, 16.7% |  e-commerce
MarketBias[407] |  2020 |  user-item interaction |  -, - , -, Low |  ModCloth, Electronics |  MSE, MAE |  ModCloth: 1.176/ 0.859, Electronics: 1.590/ 1.025 |  e-commerce
Latent factor model [408] |  2020 |  user-item interaction |  High, - , -, High |  New York Times |  F1@10, F1@20, F1@50, F1@100 |  0.5458, 0.5425, 0.5405, 0.5401 |  News recommendation
News Bias Reduction [409] |  2023 |  user-item interaction |  High, -, -, High |  MIND-small, Outbrain |  Precision@5, Recall@5, NDCG@5, Gini Index |  MIND-small: 0.65/ 0.55/ 0.60/ 0.18, Outbrain: 0.62/ 0.52/ 0.57/ 0.19 |  News recommendation
Fairness-in-Cold-Start [421] |  2023 |  user-item interaction |  -, - , -, High |  Movielens1M, Movielens20M, CiteULike, XING |  NDCG@15, NDCG@30 |  Movielens1M: 0.5516/ 0.5332, Movielens20M: 0.4408/ 0.4308, CiteULike: 0.2268/ 0.2670, XING: 0.2251/ 0.2762 |  News recommendation
FCPO [412] |  2021 |  user-item interaction |  -, - , -, High |  Movielens100k, Movielens1M |  Recall@5, F1@5, NDCG@5, Gini Index@5 |  Movielens100k: 4.740/ 4.547/ 6.031/ 98.73, Movielens1M: 2.033/ 2.668/ 4.398/ 99.81 |  e-commerce
Long Term Fairness [413] |  2019 |  user-item interaction |  -, - , -, High |  Movielens, Netflix, MSD |  NDCG@100 |  Movielens: 0.999, Netflix:0.999, MSD:0.998 |  e-commerce
NFCF [414] |  2021 |  user-item interaction |  -, - , -, High |  Movielens, Facebook |  Movielens: HR@5, NDCG@5, Facebook: HR@10, NDCG@10 |  Movielens: 0.670, 0.480, Facebook:0.551, 0.326 |  e-commerce
Contextualized Fairness [415] |  2022 |  user-item interaction |  -, - , -, Low |  XING |  HR@5, NDCG@5 |  0.581, 0.47 |  e-commerce
FAIR[416] |  2017 |  user-item interaction |  -, - , -, Low |  COMPAS, Ger. credit, SAT, XING |  NDCG |  0.9858, 0.9983, 0.9996, 1.0000 |  e-commerce
LinkedIn Talent Solutions [132] |  2018 |  user-item interaction |  -, - , -, Low |  - |  - |  - |  e-commerce

###  8.6 Miscellaneous

There are also other RS that can serve specialized purposes, as outlined
briefly below.

Group-based RS are designed to provide collective recommendations by
considering users’ shared preferences, social dynamics, and behavioral aspects
[424]. Initial studies address the cold-start problem with group-specific
methods and deep learning applications [425]. Subsequent research emphasizes
the importance of diversity, introducing algorithms to optimize group utility
and variety [426]. Advancements in group recommendations explore trust and
social dynamics by using social influence and preference relation-based
frameworks [427, 428, 429].

Some work aggregates user preferences into a unified group preference, using
both explicit and implicit feedback mechanisms [424]. Additionally, context-
aware capabilities considering significant factors for group-based scenarios
are highlighted [133]. Strategies for aggregating individual preferences, such
as aggregated voting and ensuring satisfaction for all members, are also
addressed [430, 431]. Recent research presents novel approaches to maximize
group satisfaction through least misery methods, reflecting ongoing refinement
to better cater to group needs [104].

There are also other methods, such as the Multi-Stakeholder RS approach [432]
that acknowledges that recommendations often affect multiple stakeholders
beyond the immediate users. For example, in a movie recommendation scenario,
stakeholders include not only the viewers but also the content creators,
distributors, and platforms hosting the content.

Social RS [433, 434] target the social media domain to cope with the social
overload challenge by presenting the most relevant and attractive data to
users, typically through the application of personalization techniques.
Interactive and Conversational RS [40] engage users (or groups of users) in a
dialogue to iteratively refine recommendations based on feedback. This
approach is particularly useful in group settings, where initial
recommendations may need to be negotiated among members through a series of
interactions.

Overall, these methods in group-based and social RS reflect a commitment to
improving both the precision and satisfaction of group recommendations in
increasingly complex scenarios.

##  9 Applications of Recommender Systems Across Different Domains

This section explores the technological developments and specific applications
of RS in various domains. The goal is to highlight how advancements in areas
such as GNNs, RL, LLMs, multimodal and related methods are being applied to
tackle domain-specific challenges.

#### E-commerce/E-Business

In the digital era, e-commerce platforms utilize RS to personalize the
shopping experience by recommending products based on individual preferences,
browsing and purchase histories, and cart contents, thus enhancing user
engagement and driving sales growth [105]. Advances include the integration of
big data and ML to improve satisfaction on platforms like Amazon [435], and
Alibaba [227]. Techniques such as CF and CBF, along with newer methods like
graph-based models and hypergraph ranking, refine user preference predictions
[303, 207, 211]. Sophisticated technologies like deep learning, deep
reinforcement learning, and GNNs now capture complex user behaviors [103, 181,
127, 43]. Despite these advancements, challenges like information overload and
the focus on click-through rates persist, necessitating smarter, multi-
objective RS approaches [436, 437].

#### E-Entertainment (Music, Movies, Games, Dating Apps)

Platforms like Netflix and Spotify personalize content recommendations using a
mix of CF, CBF, and hybrid approaches, employing deep learning and ML to
tailor suggestions based on user interactions and contextual factors [438,
439]. Netflix utilizes deep learning and a blend of CF and CBF to analyze
users’ interactions and viewing habits [439, 73], while Spotify leverages ML
and NLP, introducing systems like GNN for audiobooks to address data sparsity
and enhance content discovery [440, 209, 441]. The video game industry,
exemplified by STEAM, uses advanced models to offer personalized game
suggestions [442], addressing broader implications through multi-stakeholder
recommendations [432]. RS also leverage multimedia content for diverse
recommendations [20]. Innovations such as GNNs and knowledge-based methods
improve personalization [440], but challenges in dynamic consumer preferences
and the need for explainability in RS remain [41].

#### E-Health

Health RS analyze health data, lifestyle, and genetics to enhance outcomes
[443]. They address challenges like privacy and trust, and are integral to
Healthcare 4.0, focusing on personalized interventions [24, 81, 22, 444].

Systematic reviews assess health RS progress and emphasize risk management and
privacy [7, 445, 446, 447, 26]. Advances in ML and deep learning have improved
RS, with applications in diabetes, cardiac care, and beyond [448, 449, 450,
110].

Advacements in algorithms include enhanced prediction accuracy through trust
relationships and advanced ML techniques such as hybrid deep learning models
[451, 452, 453, 454, 455, 456, 457, 458]. Emerging research explores continual
learning and clustering-based techniques for improved clinical RS applications
[459, 460, 461].

#### E-Government RS

E-government utilizes electronic communication technologies to enhance service
delivery, citizen engagement, and internal processes, integrating RS to
improve user experience through AI and machine learning [115, 462, 25]. These
systems play a crucial role in smart cities by supporting information
filtering, stakeholder engagement, and decision-making [128].

Initial development of RS in e-government used CF and CBF, incorporating
hybrid models for more accurate predictions [463, 464, 465, 466]. The use of
NLP and predictive analytics enhances public service recommendations [467,
107]. Challenges such as information overload are addressed by improving CF
with negative item techniques, while newer methods like CNNs and GNNs advance
feature extraction and recommendation accuracy in industrial applications
[465, 468, 185].

#### E-Library and E-Learning

E-learning, a subset of e-libraries, utilizes electronic resources (e-books,
academic papers, journals, and other digital content) for learning and
includes a broader range of digital services for information retrieval and
research [116]. Early development in e-library RS focused on hybrid systems
combining CBF and CF techniques, often featuring bookshelf functionalities to
personalize interactions [469, 470]. These systems also use bibliographic
network representation models for citation recommendations [177, 471, 64,
472]. Advances in deep learning and context-aware recommendations have
significantly improved the efficiency of e-learning systems, surpassing
traditional methods [473, 82].

#### E-Tourism/Travel

RS have transformed travel and tourism by using vast data to provide
personalized travel suggestions, thus enhancing user satisfaction [80, 21,
474]. Major platforms like TripAdvisor and Booking.com employ CF, CBF, and
hybrid methods to offer tailored travel options [475, 476]. Continuous
advancements are needed to manage dynamic data and maintain up-to-date,
transparent recommendations that build user trust [62, 477]. Future
innovations may incorporate immersive destination previews, further
personalizing travel experiences [210, 478].

#### E-Finance

RS in finance assist investors by aligning investment options with individual
goals and risk tolerance, significantly enhancing investor engagement and
informed decision-making through analysis of financial history, risk profiles,
market trends, and economic indicators [19, 479]. Notable implementations like
the FinPathlight [480] framework enhance financial literacy and capability,
while integrating behavioral finance [481] integrates behavioral finance to
tailor financial advice based on psychological biases. Additionally, platforms
like StockTwits use sentiment analysis for more accurate investment
recommendations [479], and KiRTi employs blockchain and deep learning to
automate and secure lending processes [482]. These technologies collectively
improve the personalization of financial services, advice, and strategy
optimization [123].

Despite progress, challenges remain in handling market volatility and ensuring
transparency and trust in RS [19]. Future developments may focus on enhancing
explainability and employing predictive analytics to better anticipate market
trends and user preferences, further personalizing financial advice [483].

#### E-News

News RS curate and suggest content to users based on methodologies like CF,
CBF, and hybrid approaches, distinguishing between personalized and non-
personalized systems [59, 60]. Significant advancements in news RS have
integrated deep learning and ML to improve how news content and user data are
modeled. This includes using neural network architectures and pre-trained
language models to enhance the accuracy of content recommendations [152, 484,
485]. New techniques also explore the use of GNNs to understand complex user-
news interactions [220, 192] and innovative models like Prompt4NR for advanced
click prediction tasks [346]. The development of news RS also faces ethical
challenges, such as addressing filter bubbles, ensuring diversity, and
promoting fairness, which are crucial for maintaining user trust and system
integrity [486, 408, 487].

#### Miscellaneous

Numerous platforms have leveraged advanced RS technologies to enhance user
engagement and content personalization. YouTube employs deep neural networks
to refine its recommendation process, focusing on optimal ranking and
selection of videos [488]. Google Play utilizes both linear models and neural
networks within its Wide & Deep Learning framework to achieve a balance
between memorization and generalization [97]. LinkedIn enhances job and
content recommendation using real-time processing and scoring mechanisms,
integrating CF and deep learning to match job seekers with suitable
opportunities [132, 489]. Twitter customizes its content recommendations, like
tweets and follower suggestions, based on user behavior and preferences [490].

ByteDance has introduced innovative models for TikTok to quickly adapt
recommendations to user interactions, employing unique retrieval models and
scalable systems like Monolith, which uses collisionless embedding tables for
efficient memory usage [491, 492]. Apple has developed the Sliced Anti-
symmetric Decomposition (SAD) model to enhance collaborative filtering,
allowing more nuanced user-item interactions, and explores controlled music
production using diffusion models [493, 494]. DeepMind’s generative models
improve RS by decoding Semantic IDs from user interactions, enhancing item
retrieval and system performance [495].

Table 17: Publications by Industry in Recommendation Systems Industry |  Publications
---|---
E-commerce/E-Business |  [105, 435, 227, 303, 207, 211, 103, 181, 127, 43, 436, 437]
E-Entertainment (Music, Movies) |  [438, 439, 73, 440, 209, 441, 442, 432, 20, 41]
E-Health |  [443, 24, 81, 22, 444, 7, 445, 446, 447, 26, 448, 449, 450, 110, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461]
E-Government RS |  [115, 462, 25, 128, 463, 464, 465, 466, 467, 107, 468, 185]
E-Library and E-Learning |  [116, 469, 470, 177, 471, 64, 472, 473, 82]
E-Tourism/Travel |  [80, 21, 474, 475, 476, 62, 477, 210, 478]
E-Finance |  [19, 479, 480, 481, 479, 482, 123, 483]
E-News |  [59, 60, 152, 484, 485, 220, 192, 346, 486, 408, 487]
Miscellaneous |  [488, 97, 132, 489, 490, 491, 492, 493, 494, 495]

##  10 Discussion

###  10.1 Impact of this Research

This literature review have profound impacts on future research, industry
practices, and collaborative endeavors. This review article can serve many
purposes within academic and professional realms. The goal of this research is
beyond merely summarizing existing knowledge but also to illuminate areas
needing further investigation within RS. The detailed summaries and tables
presented in this paper can serve as educational tools that help newcomers
quickly grasp complex subjects and can be used by industry practioners to use
it as a guide. Additionally, this review tracks the development of the field,
providing insights into trends and telling future directions. For example, how
can the knowledge gained through theory can be applied to address real world
problems in industry.

We covered a comprehensive guide on many areas of RS, despite this, some areas
and fields need more coverage, which are briefly discussed below:

###  10.2 Limitations

Despite the rapid evolution and implementation of RS in theory across diverse
sectors, current methods show several critical limitations. Each application
domain, from e-commerce to e-learning, faces unique challenges that intensify
the limitations. For instance, e-commerce RS must adapt to rapidly changing
inventories and consumer trends, while e-learning systems need to account for
diverse learning styles and educational goals [105, 236, 437, 28, 496, 473,
497]. These domain-specific challenges highlight the need for RS that are not
only technically robust and ethically sound but also flexible and scalable
enough to be effectively deployed by organizations of all sizes, including
those with limited resources. These limitations span various aspects of RS,
including technical constraints, adaptability issues, and ethical concerns [7,
61, 55].

Matrix factorization-based models that are considered as standard in RS theory
struggle with capturing complex user-item interactions due to inadequate
latent feature representations and the inherent linearity of their interaction
models [4]. Neural extensions of these methods brought improvements by
incorporating non-linear relationships and capturing high-dimensional latent
features [86, 24]. However, as the volume of data grows, these deep learning-
based RS encounter their own set of challenges, particularly in maintaining
computational efficiency and scalability [498, 499, 500]. The substantial
computational resources required for training and inference of these models
pose hurdles, especially in scenarios demanding real-time recommendations. In
addition to that, many systems depend heavily on explicit user feedback (e.g.,
ratings, likes), which is often sparse and not always available, neglecting
implicit feedback signals that could enhance recommendation accuracy [141].
Furthermore, data scarcity severely affects the quality of recommendation
systems [501] . Knowledge transfer from external, data-rich domains can be a
solution to enhance the modeling capabilities and performance of RS [501].
Additionally, approaches such as data augmentation, self-supervised learning,
and knowledge graphs can enrich data environments and sustainably address data
shortages in RS development [501].

Despite advancements, many systems still fall short in effectively integrating
contextual information (e.g., time, location) and multimodal data (e.g., text,
images), limiting the depth of personalization [302, 296, 299, 298]. These RS
can incorporate biases present in their training data, leading to unfair
recommendations that favor certain groups or items over others, thus raising
ethical concerns [174, 407, 409, 171, 400, 401]. Many advanced RS, especially
those based on deep learning, operate as black boxes, offering little to no
insight into how recommendations are generated [251, 502, 129]. This lack of
transparency can degrade user trust and satisfaction.

The deployment of RS in real-life settings, particularly within mid to small
range companies, presents additional challenges. Limited resources and
technical expertise can make the deployment of sophisticated RS challenging,
intensifying issues of scalability and adaptability to rapidly changing market
conditions [503, 336, 437]. Issues with review data, including its quality,
authenticity, and the potential for manipulation, further complicate the
effective use of RS [45]. The extensive data collection necessary for
personalized recommendations raises significant privacy issues, particularly
concerning user consent and data security. Moreover, handling user review data
poses some privacy challenges, as companies must navigate the balance between
personalizing recommendations and protecting user privacy.

###  10.3 Future Perspectives

Responsible AI practises RS shape user decisions, perspectives, and actions,
underscoring the need for their design to prioritize responsibility. Recent
studies [504, 505] have raised concerns about RS potential negative impacts,
such as biasing product promotions for increased profits or facilitating the
spread of misinformation. Although there is a growing interest in adopting
responsible AI practices within the RS community, some challenges remain. Most
existing datasets lack comprehensive data on sensitive user attributes,
complicating efforts to produce fair recommendations [506]. Furthermore, the
influence of specific model architectures on the fairness of recommendations
is still not properly understood and sparsely researched, which indicates a
critical area for further investigation.

Evaluating Recommender Systems Beyond Accuracy RS are traditionally assessed
using singular metrics such as accuracy. However, this approach does not fully
encapsulate the complexity of real-world user interactions. Users demand not
only precision or recall in recommendations but also need versatility and
diversity in recommendations for better user experience [408]. Future research
should consider broadening the evaluative frameworks of RS to include metrics
that capture this diversity and serendipity.

The growing need for transparency and explainability [397] in RS suggests a
shift towards more interpretable models [403]. The integration of multimodal
data and the application of advanced learning techniques offer promising
directions to enrich user experiences, making RS not only more effective but
also more equitable and engaging. This holistic approach will ensure that RS
meet the evolving expectations of users in practical scenarios.

Beyond Statistical Correlations In this review, we explore the predominant
focus of current RS that which involves leveraging statistical correlations
from historical user data to predict preferences and make recommendations.
This method does not explicitly determine whether one factor causes another.
An active area of research is causality in RS involves identifying how
specific factors, like user behavior or item features, directly cause changes
in recommendations [507]. For instance, researchers might investigate whether
increasing the exposure of action movies leads to a higher viewership of this
genre [507].

Computation and Storage Resources Despite the benefits of aggregating user-
item interaction data from various users in a central database to leverage
collaborative information for recommendations, this approach has some
drawbacks. It is time-consuming, demands substantial communication and storage
resources, and raises serious privacy and security concerns. A new line of
research is to perform on-device recommendation (DeviceRS) which is a small
minimal model that can be trained with lower computation and storage resources
[508, 509]. This line of research is still in its early stages of development
and deals with several open questions and challenges. Finding an efficient way
to use collaborative information from other users while keeping computation,
storage, and data exposure low, and considering the differences in data on
each user’s device, remains an open challenge.

Generative AI  With the rise of generative AI models like ChatGPT, researchers
are exploring their potential to enhance various fields, including RS.
Conversational RS, which provide suggestions through dialogue, are gaining
popularity, and we have dedicated an entire sub-section to this exciting
development. However, it is essential to emphasize that while leveraging
generative AI, we must ensure the outputs are safe and adhere to AI safety and
responsible practices [510]. This not only maximizes the benefits but also
mitigates potential risks associated with these methods.

##  11 Conclusion

In this survey, we have reviewed the notable methodologies, applications, and
challenges of RS in both academic and industrial contexts. We proposed a
framework to categorize RS publications based on modeling techniques and their
applications. The integration of RS with state-of-the-art methods such as deep
learning, graph neural networks, and LLMs demonstrates the evolution in this
field and highlights its impact on improving user experiences in diverse
domains, including e-commerce, finance, media streaming, and personalized
education. Despite notable advancements, we still face challenges, including
data sparsity, privacy issues, and the need for systems that are both
adaptable and explainable. This survey aims to bridge theoretical advances and
algorithmic developments with practical applications, helping the industry
achieve scalability and immediate business impact. Our goal is to strengthen
collaborations between academia and industry, which is essential to translate
theoretical progress into practical applications.

## Acknowledgements

The authors wish to extend their profound gratitude to Scott Sanner, whose
expert guidance and invaluable input were instrumental throughout the research
and writing of this survey. His deep insights into the nuances of recommender
systems significantly enhanced the quality of our work. We also appreciate the
advice provided by Andres Rojas, Ali Taiyeb, and Deval Pandya during critical
stages of this project. Additionally, we thank Veronica Chatrath for her
contributions in the ideation phase. This survey could not have been
accomplished without their support.

## References

  * [1] Charu C. Aggarwal.  Recommender Systems The Textbook, volume 39.  Springer Cham, 2016.
  * [2] Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci.  Mediation of user models for enhanced personalization in recommender systems.  User Modeling and User-Adapted Interaction, 18(3), 2008.
  * [3] Gediminas Adomavicius and Alexander Tuzhilin.  Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, 2005.
  * [4] Francesco Ricci, Lior Rokach, and Bracha Shapira.  Recommender Systems: Techniques, Applications, and Challenges.  In Recommender Systems Handbook: Third Edition, chapter Chapter 1, pages 1–35. Springer, New York, NY, 2022.
  * [5] Xavier Amatriain and Justin Basilico.  Past, present, and future of recommender systems: An industry perspective.  In RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 2016.
  * [6] Shristi Shakya Khanal, P. W.C. Prasad, Abeer Alsadoon, and Angelika Maag.  A systematic review: machine learning based recommendation systems for e-learning.  Education and Information Technologies, 25(4), 2020.
  * [7] Maryam Etemadi, Sepideh Bazzaz Abkenar, Ahmad Ahmadzadeh, Mostafa Haghi Kashani, Parvaneh Asghari, Mohammad Akbari, and Ebrahim Mahdipour.  A systematic review of healthcare recommender systems: Open issues, challenges, and techniques, 2023.
  * [8] Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, and Qing Li.  Recommender Systems in the Era of Large Language Models (LLMs).  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, page 1, 2023.
  * [9] ACM Recommender Systems Conference.  ACM RecSys Conferences, 2024.
  * [10] Ian MacKenzie, Chris Meyer, and Steve Noble.  How Retailers can keep up with Consumers.  McKinsey \& Company - Retail Insights, (October), 2013.
  * [11] Mansoor Iqbal.  Netflix Revenue and Usage Statistics (2024) - Business of Apps, 2024.
  * [12] Precision Reports.  Worldwide Market Research Report, and Industry Analysis, 2024.
  * [13] Robin Burke, Alexander Felfernig, and Mehmet H. Göker.  Recommender systems: An overview.  AI Magazine, 32(3), 2011.
  * [14] Xavier Amatriain and Justin Basilico.  Recommender Systems in Industry: A Netflix Case Study.  In Recommender Systems Handbook, chapter Chapter 11, pages 385–419. Springer, Boston, MA, 2016.
  * [15] Prem Melville and Vikas Sindhwani.  Recommender Systems.  In Encyclopedia of Machine Learning, pages 829–838. Springer, Boston, MA, Boston, MA, 2010.
  * [16] Alvise De Biasio, Nicolò Navarin, and Dietmar Jannach.  Economic recommender systems – a systematic review.  Electronic Commerce Research and Applications, 63:101352, 1 2024.
  * [17] Ankit Thakkar and Kinjal Chaudhari.  A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions, 2021.
  * [18] Rajat Kumar Behera, Angappa Gunasekaran, Shivam Gupta, Shampy Kamboj, and Pradip Kumar Bala.  Personalized digital marketing recommender engine.  Journal of Retailing and Consumer Services, 53, 2020.
  * [19] Marwa Sharaf, Ezz El Din Hemdan, Ayman El-Sayed, and Nirmeen A. El-Bahnasawy.  A survey on recommendation systems for financial services.  Multimedia Tools and Applications, 81(12), 2022.
  * [20] Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, and Gabriella Pasi.  Recommender Systems Leveraging Multimedia Content.  ACM Computing Surveys, 53(5), 2020.
  * [21] Kinjal Chaudhari and Ankit Thakkar.  A Comprehensive Survey on Travel Recommender Systems.  Archives of Computational Methods in Engineering, 27(5), 2020.
  * [22] Robin De Croon, Leen Van Houdt, Nyi Nyi Htun, Gregor Štiglic, Vero Vanden Abeele, and Katrien Verbert.  Health recommender systems: Systematic review, 2021.
  * [23] Zafar Ali, Yi Huang, Irfan Ullah, Junlan Feng, Chao Deng, Nimbeshaho Thierry, Asad Khan, Asim Ullah Jan, Xiaoli Shen, Wu Rui, and Guilin Qi.  Deep Learning for Medication Recommendation: A Systematic Survey, 2023.
  * [24] Jayita Saha, Chandreyee Chowdhury, and Suparna Biswas.  Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics.  In Deep Learning Techniques for Biomedical and Health Informatics, chapter Chapter 6, pages 101–126. Springer, Cham, 2020.
  * [25] Kei Long Cheung, Dilara Durusu, Xincheng Sui, and Hein de Vries.  How recommender systems could support and enhance computer-tailored digital health programs: A scoping review.  Digital health, 5, 1 2019.
  * [26] Yue Sun, Jia Zhou, Mengmeng Ji, Lusi Pei, and Zhiwen Wang.  Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping, 2023.
  * [27] John K. Tarus, Zhendong Niu, and Ghulam Mustafa.  Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning.  Artificial Intelligence Review, 50(1), 2018.
  * [28] Aleksandra Klašnja-Milićević, Mirjana Ivanović, and Alexandros Nanopoulos.  Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions.  Artificial Intelligence Review, 44(4), 2015.
  * [29] Ivens Portugal, Paulo Alencar, and Donald Cowan.  The use of machine learning algorithms in recommender systems: A systematic review, 2018.
  * [30] Chuan Qin, Hengshu Zhu, Fuzhen Zhuang, Qingyu Guo, Qi Zhang, Le Zhang, Chao Wang, Enhong Chen, and Hui Xiong.  A survey on knowledge graph-based recommender systems, 2020.
  * [31] Yongfeng Zhang and Xu Chen.  Explainable recommendation: A survey and new perspectives, 2020.
  * [32] Saurabh Kulkarni and Sunil F. Rodd.  Context Aware Recommendation Systems: A review of the state of the art techniques, 2020.
  * [33] Shaina Raza and Chen Ding.  Progress in context-aware recommender systems - An overview, 2019.
  * [34] Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang.  Collaborative filtering and deep learning based recommendation system for cold start items.  Expert Systems with Applications, 69, 2017.
  * [35] Rui Chen, Qingyi Hua, Yan Shuo Chang, Bo Wang, Lei Zhang, and Xiangjie Kong.  A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks.  IEEE Access, 6, 2018.
  * [36] Erion Çano and Maurizio Morisio.  Hybrid Recommender Systems: A Systematic Literature Review.  Intelligent Data Analysis, 21(6):1487–1524, 1 2019.
  * [37] Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach.  Sequence-aware recommender systems, 2018.
  * [38] Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Defu Lian.  A Survey on Session-based Recommender Systems.  ACM Computing Surveys, 54(7), 2022.
  * [39] Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach.  Empirical analysis of session-based recommendation algorithms: A comparison of neural and non-neural approaches.  User Modeling and User-Adapted Interaction, 31(1), 2021.
  * [40] Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen.  A Survey on Conversational Recommender Systems, 2021.
  * [41] Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, and Manuel Moussallam.  Explainability in music recommender systems.  AI Magazine, 43(2), 2022.
  * [42] Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi.  Current challenges and visions in music recommender systems research.  International Journal of Multimedia Information Retrieval, 7:95–116, 2018.
  * [43] M. Mehdi Afsar, Trafford Crump, and Behrouz Far.  Reinforcement Learning based Recommender Systems: A Survey.  ACM Computing Surveys, 55(7), 2022.
  * [44] Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra.  A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks, 2021.
  * [45] Mehdi Srifi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline.  Recommender systems based on collaborative filtering using review texts-A survey, 2020.
  * [46] Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui.  Graph Neural Networks in Recommender Systems: A Survey.  ACM Computing Surveys, 55(5), 2022.
  * [47] Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, and Yong Li.  A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions.  ACM Transactions on Recommender Systems, 1(1), 2023.
  * [48] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu.  A Comprehensive Survey on Graph Neural Networks.  IEEE Transactions on Neural Networks and Learning Systems, 32(1), 2021.
  * [49] Zeynep Batmaz, Ali Yurekli, Alper Bilge, and Cihan Kaleli.  A review on deep learning for recommender systems: challenges and remedies.  Artificial Intelligence Review, 52(1), 2019.
  * [50] Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen.  Large Language Models for Generative Recommendation: A Survey and Visionary Discussions.  9 2023.
  * [51] Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen.  A Survey on Large Language Models for Recommendation.  5 2023.
  * [52] Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, and Jie Fu.  Pre-trained Language Models in Biomedical Domain: A Systematic Survey.  ACM Computing Surveys, 56(3):57, 10 2021.
  * [53] Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui.  A survey for in-context learning.  Computer Science, 2022.
  * [54] Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long.  Graph Neural Networks for Natural Language Processing: A Survey.  Foundations and Trends in Machine Learning, 16(2), 2023.
  * [55] Peng Liu, Lemei Zhang, and Jon Atle Gulla.  Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems.  Transactions of the Association for Computational Linguistics, 11:1553–1571, 12 2023.
  * [56] Hai Ha Do, P. W.C. Prasad, Angelika Maag, and Abeer Alsadoon.  Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review, 2019.
  * [57] Deepjyoti Roy and Mala Dutta.  A systematic review and research perspective on recommender systems.  Journal of Big Data, 9(1), 2022.
  * [58] Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac.  News recommender systems – Survey and roads ahead.  Information Processing and Management, 54(6), 2018.
  * [59] Shaina Raza and Chen Ding.  News recommender system: a review of recent progress, challenges, and opportunities.  Artificial Intelligence Review, 55(1), 2022.
  * [60] Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie.  Personalized News Recommendation: Methods and Challenges.  ACM Transactions on Information Systems, 41(1), 2023.
  * [61] Yassine Himeur, Shahab Saquib Sohail, Faycal Bensaali, Abbes Amira, and Mamoun Alazab.  Latest trends of security and privacy in recommender systems: a comprehensive review and future perspectives.  Computers & Security, 118:102746, 2022.
  * [62] Joy Lal Sarkar, Abhishek Majumder, Chhabi Rani Panigrahi, Sudipta Roy, and Bibudhendu Pati.  Tourism recommendation system: a survey and future research directions.  Multimedia Tools and Applications, 82(6), 2023.
  * [63] Eva Zangerle and Christine Bauer.  Evaluating Recommender Systems: Survey and Framework.  2022\.
  * [64] Zafar Ali, Guilin Qi, Pavlos Kefalas, Waheed Ahmad Abro, and Bahadar Ali.  A graph-based taxonomy of citation recommendation models.  Artificial Intelligence Review, 53(7), 2020.
  * [65] Govind Kumar Jha, Manish Gaur, Preetish Ranjan, and Hardeo Kumar Thakur.  A survey on trustworthy model of recommender system.  International Journal of System Assurance Engineering and Management, 14, 2023.
  * [66] Mario Casillo, Francesco Colace, Dajana Conte, · Marco Lombardi, Domenico Santaniello, and Carmine Valentino.  Context-aware recommender systems and cultural heritage: a survey.  Journal of Ambient Intelligence and Humanized Computing, 14:3109–3127, 2023.
  * [67] Elaine Rich.  User modeling via stereotypes.  Cognitive Science, 3(4):329–354, 10 1979.
  * [68] Jussi Karlgren.  An algebra for recommendations : Using reader data as a basis for measuring document proximity.  1990\.
  * [69] Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl.  GroupLens: An open architecture for collaborative filtering of netnews.  Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pages 175–186, 10 1994.
  * [70] Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl.  An algorithmic framework for performing collaborative filtering.  In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, 1999.
  * [71] Joeran Beel Docear, Stefan Langer, Marcel Genzmehr, Bela Gipp, Corinna Breitinger, and Andreas Nürnberger.  Research Paper Recommender System Evaluation: A Quantitative Literature Survey.
  * [72] Yandi Xia, Giuseppe Di Fabbrizio, Shikhar Vaibhav, Ankur Datta, and Ankur 2017 Datta.  A Content-based Recommender System for E-commerce OOers and Coupons.  SIGIR, 2017.
  * [73] How Netflix’s Recommendations System Works | Netflix Help Center.
  * [74] Angela Carrera-Rivera, William Ochoa, Felix Larrinaga, and Ganix Lasa.  How-to conduct a systematic literature review: A quick guide for computer science research.  MethodsX, 9, 2022.
  * [75] ACM RecSys.  Workshop on Deep Learning for Recommender Systems.  In Proceedings of the 11th ACM Conference on Recommender Systems, Como, Italy, 2017.
  * [76] Farah Tawfiq Abdul Hussien, Abdul Monem S. Rahma, and Hala Bahjat Abdul Wahab.  Recommendation Systems for E-commerce Systems An Overview.  In Journal of Physics: Conference Series, volume 1897, 2021.
  * [77] Ke Chen, Beici Liang, Xiaoshuan Ma, and Minwei Gu.  Learning audio embeddings with user listening data for content-based music recommendation.  In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, volume 2021-June, 2021.
  * [78] Gustavo Penha and Claudia Hauff.  What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation.  RecSys 2020 - 14th ACM Conference on Recommender Systems, 20:388–397, 9 2020.
  * [79] Shaina Raza.  Automatic fake news detection in political platforms-a transformer-based approach.  In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 68–78, 2021.
  * [80] Rula A. Hamid, A. S. Albahri, Jwan K. Alwan, Z. T. Al-Qaysi, O. S. Albahri, A. A. Zaidan, Alhamzah Alnoor, A. H. Alamoodi, and B. B. Zaidan.  How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management, 2021.
  * [81] Thi Ngoc Trang Tran, Alexander Felfernig, Christoph Trattner, and Andreas Holzinger.  Recommender systems in the healthcare domain: state-of-the-art and research issues.  Journal of Intelligent Information Systems, 57(1), 2021.
  * [82] Nur W. Rahayu, Ridi Ferdiana, and Sri S. Kusumawardani.  A systematic review of ontology use in E-Learning recommender system, 2022.
  * [83] Michael J. Pazzani and Daniel Billsus.  Content-based recommendation systems.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4321 LNCS:325–341, 2007.
  * [84] Peter D. Turney and Patrick Pantel.  From frequency to meaning: Vector space models of semantics.  Journal of Artificial Intelligence Research, 37, 2010.
  * [85] Alexandrin Popescul, David M Pennock, and Steve Lawrence.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments.  Artificial Intelligence, 2001(Uai):437–444, 2001.
  * [86] Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay.  Deep learning based recommender system: A survey and new perspectives, 2019.
  * [87] Lei Zheng, Vahid Noroozi, and Philip S. Yu.  Joint deep modeling of users and items using reviews for recommendation.  In WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017.
  * [88] Chandra Bhagavatula, Sergey Feldman, Russell Power, and Waleed Ammar.  Content-based citation recommendation.  In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, volume 1, 2018.
  * [89] Kevin Joseph and Hui Jiang.  Content based news recommendation via shortest entity distance over knowledge graphs.  In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 2019.
  * [90] Fuhu Deng, Panlong Ren, Zhen Qin, Gu Huang, and Zhiguang Qin.  Leveraging Image Visual Features in Content-Based Recommender System.  Scientific Programming, 2018, 2018.
  * [91] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl.  Item-based collaborative filtering recommendation algorithms.  In Proceedings of the 10th International Conference on World Wide Web, WWW 2001, 2001.
  * [92] Yehuda Koren, Robert Bell, and Chris Volinsky.  Matrix factorization techniques for recommender systems.  Computer, 42(8), 2009.
  * [93] Steffen Rendle.  Factorization machines with libFM.  ACM Transactions on Intelligent Systems and Technology, 3(3), 2012.
  * [94] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua.  Neural collaborative filtering.  In Proceedings of the 26th international conference on world wide web, pages 173–182, 2017.
  * [95] Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, and Charu Aggarwal.  Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities.  In SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
  * [96] Poonam B.Thorat, R. M. Goudar, and Sunita Barve.  Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System.  International Journal of Computer Applications, 110(4), 2015.
  * [97] Heng Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah.  Wide \& deep learning for recommender systems.  In ACM International Conference Proceeding Series, volume 15-September-2016, 2016.
  * [98] Xiangnan He and Tat Seng Chua.  Neural factorization machines for sparse predictive analytics.  In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017.
  * [99] Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He.  DeepFM: A factorization-machine based neural network for CTR prediction.  In IJCAI International Joint Conference on Artificial Intelligence, volume 0, 2017.
  * [100] Ruoxi Wang, Gang Fu, Bin Fu, and Mingliang Wang.  Deep and cross network for ad click predictions.  In 2017 AdKDD and TargetAd - In conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2017, 2017.
  * [101] Khalid Haruna, Maizatul Akmar Ismail, Abdullahi Baffa Bichi, Victor Chang, Sutrisna Wibawa, and Tutut Herawan.  A citation-based recommender system for scholarly paper recommendation.  In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10960 LNCS, 2018.
  * [102] Tessy Badriyah, Erry Tri Wijayanto, Iwan Syarif, and Prima Kristalina.  A hybrid recommendation system for E-commerce based on product description and user profile.  In 7th International Conference on Innovative Computing Technology, INTECH 2017, 2017.
  * [103] Mingsheng Fu, Hong Qu, Zhang Yi, Li Lu, and Yongsheng Liu.  A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System.  IEEE Transactions on Cybernetics, 49(3), 2019.
  * [104] Sabrine Ben Abdrbbah.  A Novel Recommendation Approach For Groups Based On Aggregating Top-k Lists.  Procedia Computer Science, 225:3067–3076, 1 2023.
  * [105] Kangning Wei, Jinghua Huang, and Shaohong Fu.  A survey of E-commerce recommender systems.  In Proceedings - ICSSSM’07: 2007 International Conference on Service Systems and Service Management, 2007.
  * [106] Rajesh K. Jha, Sujoy Bag, Debbani Koley, Giridhar Reddy Bojja, and Subhas Barman.  An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning.  Intelligent Systems with Applications, 18:200218, 5 2023.
  * [107] Omar Saeed Al-Mushayt.  Automating E-Government Services with Artificial Intelligence.  IEEE Access, 7, 2019.
  * [108] Robin Burke, Nasim Sonboli, and Aldo Ordoñez-Gauger.  Balanced Neighborhoods for Multi-sided Fairness in Recommendation.  In Proceedings of Machine Learning Research, volume 81, 2018.
  * [109] Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He.  Bias and Debias in Recommender System: A Survey and Future Directions.  ACM Transactions on Information Systems, 41(3), 2023.
  * [110] Sandeep K. Raghuwanshi and R. K. Pateriya.  Collaborative Filtering Techniques in Recommendation Systems.  In Data, Engineering and Applications, volume 1, chapter Chapter 2, pages 11–21. Springer, Singapore, 2019.
  * [111] Xiao Yu, Xiang Ren, Quanquan Gu, Yizhou Sun, and Jiawei Han.  Collaborative Filtering with Entity Similarity Regularization in Heterogeneous Information Networks.  In Proc. of IJCAI-13 HINA workshop (IJCAI-HINA’13), 2013.
  * [112] Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei Ying Ma.  Collaborative knowledge base embedding for recommender systems.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume 13-17-August-2016, 2016.
  * [113] Hong Jian Xue, Xin Yu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen.  Deep matrix factorization models for recommender systems.  In IJCAI International Joint Conference on Artificial Intelligence, volume 0, 2017.
  * [114] Qiang Liu, Shu Wu, and Liang Wang.  Deepstyle: Learning user preferences for visual recommendation.  In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017.
  * [115] Caie Xu, Lisha Xu, Yingying Lu, Huan Xu, and Zhongliang Zhu.  E-government recommendation algorithm based on probabilistic semantic cluster analysis in combination of improved collaborative filtering in big-data environment of government affairs.  Personal and Ubiquitous Computing, 23(3-4), 2019.
  * [116] Martin Ebner.  E-learning 2.0 = e-learning 1.0 + Web 2.0?  In Proceedings - Second International Conference on Availability, Reliability and Security, ARES 2007, 2007.
  * [117] Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, and Jiliang Tang.  Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation.  Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX), 1, 2 2024.
  * [118] Luyi Ma, Nimesh Sinha, Parth Vajge, Jason H.D. Cho, Sushant Kumar, and Kannan Achan.  Event-based Product Carousel Recommendation with Query-Click Graph.  In Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 2021.
  * [119] Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song.  Generative adversarial user model for reinforcement learning based recommendation system.  In 36th International Conference on Machine Learning, ICML 2019, volume 2019-June, 2019.
  * [120] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec.  Graph convolutional neural networks for web-scale recommender systems.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
  * [121] Yao Cai, Fei Yu, Manish Kumar, Roderick Gladney, and Javed Mostafa.  Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review.  International Journal of Environmental Research and Public Health, 19(22), 2022.
  * [122] Xiao Wang, Houye Ji, Peng Cui, P. Yu, Chuan Shi, Bai Wang, and Yanfang Ye.  Heterogeneous graph attention network.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [123] Wanying Ding, Vinay K. Chaudhri, Naren Chittar, and Krishna Konakanchi.  JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase.  In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, volume 17B, 2021.
  * [124] K. Shailaja, B. Seetharamulu, and M. A. Jabbar.  Machine Learning in Healthcare: A Review.  In Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018, 2018.
  * [125] Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie.  Neural news recommendation with long- And short-term user representations.  In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020.
  * [126] Mirko Perano, Gian Luca Casali, Yulin Liu, and Tindara Abbate.  Professional reviews as service: A mix method approach to assess the value of recommender systems in the entertainment industry.  Technological Forecasting and Social Change, 169, 2021.
  * [127] Xiang Ma, Xiaojiang Lei, Guoshuai Zhao, and Xueming Qian.  Rating prediction by exploring user’s preference and sentiment.  Multimedia Tools and Applications, 77(6), 2018.
  * [128] María E. Cortés-Cediel, Iván Cantador, and Olga Gil.  Recommender systems for e-governance in smart cities: State of the art and research opportunities.  In ACM International Conference Proceeding Series, 2017.
  * [129] Yanzhang Lyu, Hongzhi Yin, Jun Liu, Mengyue Liu, Huan Liu, and Shizhuo Deng.  Reliable recommendation with review-level explanations.  In Proceedings - International Conference on Data Engineering, volume 2021-April, 2021.
  * [130] Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He.  A simple convolutional generative network for next item recommendation.  WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, pages 582–590, 1 2019.
  * [131] Yu Lei, Zhitao Wang, Wenjie Li, and Hongbin Pei.  Social attentive deep Q-network for recommendation.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [132] Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, and Krishnaram Kenthapadi.  Talent search and recommendation systems at Linkedin: Practical challenges and lessons learned.  In 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, 2018.
  * [133] Yilena Pérez-Almaguer, Raciel Yera, Ahmad A. Alzahrani, and Luis Martínez.  Content-based group recommender systems: A general taxonomy and further improvements.  Expert Systems with Applications, 184, 2021.
  * [134] Chanwoo Jeong, Sion Jang, Eunjeong Park, and Sungchul Choi.  A context-aware citation recommendation model with BERT and graph convolutional networks.  Scientometrics, 124(3), 2020.
  * [135] Srs Reddy, Sravani Nalluri, Subramanyam Kunisetti, S. Ashok, and B. Venkatesh.  Content-based movie recommendation system using genre correlation.  In Smart Innovation, Systems and Technologies, volume 105, 2019.
  * [136] Bogdan Walek and Petra Spackova.  Content-Based Recommender System for Online Stores Using Expert System.  In Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018, 2018.
  * [137] Nagagopiraju Vullam, Sai Srinivas Vellela, Reddy Venkateswara, M. Venkateswara Rao, S. K. Khader Basha, and D. Roja.  Multi-Agent Personalized Recommendation System in E-Commerce based on User.  In Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, 2023.
  * [138] Harsh Khatter, Shifa Arif, Utsav Singh, Sarthak Mathur, and Satvik Jain.  Product Recommendation System for E-Commerce using Collaborative Filtering and Textual Clustering.  In Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, 2021.
  * [139] Xinyan Fan, Zheng Liu, Jianxun Lian, Wayne Xin Zhao, Xing Xie, and Ji Rong Wen.  Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation.  In SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.
  * [140] Robin Burke.  Hybrid web recommender systems.  The adaptive web, pages 377–408, 2007.
  * [141] Jianxun Lian, Zhongxia Chen, Xiaohuan Zhou, Xing Xie, Fuzheng Zhang, and Guangzhong Sun.  xDeepFM: Combining explicit and implicit feature interactions for recommender systems.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
  * [142] Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji Rong Wen.  Filter-enhanced MLP is All You Need for Sequential Recommendation.  In WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022.
  * [143] Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, and Zhenhua Dong.  FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction.  In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, volume 37, 2023.
  * [144] Suvash Sedhain, Aditya Krishna Menony, Scott Sannery, and Lexing Xie.  AutoRec: Autoencoders meet collaborative filtering.  In WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web, 2015.
  * [145] Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara.  Variational autoencoders for collaborative filtering.  In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, 2018.
  * [146] Wen Zhang, Yuhang Du, Taketoshi Yoshida, and Ye Yang.  DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function.  Information Sciences, 470, 2019.
  * [147] Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko.  RecVAE: A new variational autoencoder for top-n recommendations with implicit feedback.  In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, 2020.
  * [148] Jinhyeok Park, Dain Kim, and Dongwoo Kim.  Item-based variational auto-encoder for fair music recommendation.  In CEUR Workshop Proceedings, volume 3318, 2022.
  * [149] Yaochen Zhu and Zhenzhong Chen.  Variational Bandwidth Auto-Encoder for Hybrid Recommender Systems.  IEEE Transactions on Knowledge and Data Engineering, 35(5), 2023.
  * [150] Kamal Berahmand, Fatemeh Daneshfar, · Elaheh, Sadat Salehi, Yuefeng Li, Yue Xu, Elaheh Sadat Salehi, and K Berahmand.  Autoencoders and their applications in machine learning: a survey.  Artificial Intelligence Review, 123.
  * [151] A. Razia Sulthana, Maulika Gupta, Shruthi Subramanian, and Sakina Mirza.  Improvising the performance of image-based recommendation system using convolution neural networks and deep learning.  Soft Computing, 24(19):14531–14544, 10 2020.
  * [152] Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo.  DKN: Deep knowledge-aware network for news recommendation.  In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, 2018.
  * [153] Jordi Pons and Xavier Serra.  musicnn: Pre-trained convolutional neural networks for music audio tagging.  Papers with Code, 9 2019.
  * [154] Ming Chen, Tianyi Ma, and Xiuze Zhou.  CoCNN: Co-occurrence CNN for recommendation.  Expert Systems with Applications, 195, 2022.
  * [155] Yu Wang, Yuying Zhao, Yi Zhang, and Tyler Derr.  Collaboration-Aware Graph Convolutional Network for Recommender Systems.  ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, pages 91–101, 4 2023.
  * [156] Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, and Laith Farhan.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.  Journal of Big Data 2021 8:1, 8(1):1–74, 3 2021.
  * [157] Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long Kai Huang, and Chi Xu.  Recurrent knowledge graph embedding for effective recommendation.  In RecSys 2018 - 12th ACM Conference on Recommender Systems, 2018.
  * [158] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk.  Session-based recommendations with recurrent neural networks.  In 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, 2016.
  * [159] Balázs Hidasi and Alexandros Karatzoglou.  Recurrent neural networks with top-k gains for session-based recommendations.  In Proceedings of the 27th ACM international conference on information and knowledge management, pages 843–852, 2018.
  * [160] Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma.  Neural attentive session-based recommendation.  In International Conference on Information and Knowledge Management, Proceedings, volume Part F131841, 2017.
  * [161] Wang Cheng Kang and Julian McAuley.  Self-Attentive Sequential Recommendation.  In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2018-November, 2018.
  * [162] Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, and Li Guo.  DAN: Deep attention neural network for news recommendation.  In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 2019.
  * [163] Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang.  Deep session interest network for click-through rate prediction.  In IJCAI International Joint Conference on Artificial Intelligence, volume 2019-August, 2019.
  * [164] Shaina Raza and Chen Ding.  Deep Neural Network to Tradeoff between Accuracy and Diversity in a News Recommender System.  Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, pages 5246–5256, 2021.
  * [165] Ngo Xuan Bach, Dang Hoang Long, and Tu Minh Phuong.  Recurrent convolutional networks for session-based recommendations.  Neurocomputing, 411, 2020.
  * [166] Xiaokang Zhou, Yue Li, and Wei Liang.  CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support.  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3):912–921, 5 2021.
  * [167] Lei Guo, Jinyu Zhang, Tong Chen, Xinhua Wang, and Hongzhi Yin.  Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation.  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, XX:1, 2022.
  * [168] Na Zhao, Zhen Long, Jian Wang, and Zhi Dan Zhao.  AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder.  Knowledge-Based Systems, 259, 2023.
  * [169] Zhiwei Guo and Heng Wang.  A Deep Graph Neural Network-Based Mechanism for Social Recommendations.  IEEE Transactions on Industrial Informatics, 17(4), 2021.
  * [170] Ruiping Yin, Kan Li, Guangquan Zhang, and Jie Lu.  A deeper graph neural network for recommender systems.  Knowledge-Based Systems, 185, 2019.
  * [171] Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke.  A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems.  ACM Transactions on Information Systems, 40(2), 2022.
  * [172] Siwei Liu, Iadh Ounis, Craig MacDonald, and Zaiqiao Meng.  A Heterogeneous Graph Neural Model for Cold-start Recommendation.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [173] Xiaohan Zou.  A Survey on Application of Knowledge Graph.  In Journal of Physics: Conference Series, volume 1487, 2020.
  * [174] Michael D Ekstrand, Mucun Tian, Jennifer D Ekstrand, Oghenemaro Anuyah, David Mcneill, and Maria Soledad Pera.  All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness.  Proceedings of Machine Learning Research, 81, 2018.
  * [175] Bin Liang, Hang Su, Lin Gui, Erik Cambria, and Ruifeng Xu.  Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks.  Knowledge-Based Systems, 235, 2022.
  * [176] Yuting Ye, Jingren Zhou, Xuwu Wang, Yanghua Xiao, Jiangchao Yao, Kunyang Jia, and Hongxia Yang.  Bayes Embedding (BEM): Refining representation by integrating knowledge graphs and behavior-specific networks.  In International Conference on Information and Knowledge Management, Proceedings, 2019.
  * [177] Xiaoyan Cai, Yu Zheng, Libin Yang, Tao Dai, and Lantian Guo.  Bibliographic Network Representation Based Personalized Citation Recommendation.  IEEE Access, 7, 2019.
  * [178] Avishek Joey Bose and William L. Hamilton.  Compositional fairness constraints for graph embeddings.  In 36th International Conference on Machine Learning, ICML 2019, volume 2019-June, 2019.
  * [179] Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang.  Dynamic Graph Neural Networks for Sequential Recommendation.  IEEE Transactions on Knowledge and Data Engineering, 35(5), 2023.
  * [180] Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat Seng Chua.  Explainable reasoning over knowledge graphs for recommendation.  In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 2019.
  * [181] Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin.  Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks.  ACM Transactions on Information Systems, 38(3), 2020.
  * [182] Tao Dai, Li Zhu, Xiaoyan Cai, Shirui Pan, and Sheng Yuan.  Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network.  Journal of Ambient Intelligence and Humanized Computing, 9(4), 2018.
  * [183] Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard De Melo.  Fairness-Aware Explainable Recommendation over Knowledge Graphs.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [184] Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang.  Fi-GNN: Modeling feature interactions via graph neural networks for CTR prediction.  In International Conference on Information and Knowledge Management, Proceedings, 2019.
  * [185] Lingyuan Kong, Hao Ding, and Guangwei Hu.  GCNSLIM: Graph Convolutional Network with Sparse Linear Methods for E-government Service Recommendation.  Knowledge-Based Systems, Volume 292, 5 2023.
  * [186] Ziyang Wang, Wei Wei, Gao Cong, Xiao Li Li, Xian Ling Mao, and Minghui Qiu.  Global Context Enhanced Graph Neural Networks for Session-based Recommendation.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [187] Chun Liu, Yuxiang Li, Hong Lin, and Chaojie Zhang.  GNNRec: gated graph neural network for session-based social recommendation model.  Journal of Intelligent Information Systems, 60(1), 2023.
  * [188] Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, and Xing Xie.  Graph convolution machine for context-aware recommender system.  Frontiers of Computer Science, 16(6), 2022.
  * [189] Van Den Rianne Berg, Thomas N Kipf, and Max Welling.  Graph Convolutional Matrix Completion.  Papers with Code, 2017.
  * [190] Qiang He, Xinkai Li, and Biao Cai.  Graph neural network recommendation algorithm based on improved dual tower model.  Scientific Reports 2024 14:1, 14(1):1–13, 2 2024.
  * [191] Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin.  Graph neural networks for social recommendation.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [192] Zhaopeng Qiu, Yunfan Hu, and Xian Wu.  Graph Neural News Recommendation with User Existing and Potential Interest Modeling.  ACM Transactions on Knowledge Discovery from Data, 16(5), 2022.
  * [193] Meng Liu, Jianjun Li, Ke Liu, Chaoyang Wang, Pan Peng, Guohui Li, Yongjing Cheng, Guohui Jia, and Wei Xie.  Graph-ICF: Item-based collaborative filtering based on graph neural network.  Knowledge-Based Systems, 251, 2022.
  * [194] William L Hamilton, Rex Ying, and Jure Leskovec.  Inductive Representation Learning on Large Graphs.  Advances in Neural Information Processing Systems, 2017.
  * [195] Jun Zhao, Zhou Zhou, Wei Ning, Ziyu Guan, Guang Qiu, Wei Zhao, and Xiaofei He.  IntentGC: A scalable graph convolution framework fusing heterogeneous information for recommendation.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [196] Weizhi Ma, Woojeong Jin, Min Zhang, Chenyang Wang, Yue Cao, Yiqun Liu, Shaoping Ma, and Xiang Ren.  Jointly learning explainable rules for recommendation with knowledge graph.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [197] Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat Seng Chua.  KGAT: Knowledge graph attention network for recommendation.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [198] Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao.  Knowledge graph embedding via dynamic mapping matrix.  In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, volume 1, 2015.
  * [199] Heiko Paulheim.  Knowledge graph refinement: A survey of approaches and evaluation methods.  Semantic Web, 8(3), 2017.
  * [200] Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang.  Learning fair representations for recommendation: A graph-based perspective.  In The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, 2021.
  * [201] Alberto García-Durán and Mathias Niepert.  Learning graph representations with embedding propagation.  In Advances in Neural Information Processing Systems, volume 2017-December, 2017.
  * [202] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong Dong Zhang, and Meng Wang.  LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [203] Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, and Jie Tang.  MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021.
  * [204] Yinwei Wei, Xiangnan He, Xiang Wang, Richang Hong, Liqiang Nie, and Tat Seng Chua.  MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video.  In MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 2019.
  * [205] Ke Wang, Yanmin Zhu, Tianzi Zang, Chunyang Wang, Kuan Liu, and Peibo Ma.  Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation.  ACM Transactions on Information Systems, 42(2), 2023.
  * [206] Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo.  Multi-task feature learning for knowledge graph enhanced recommendation.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [207] Mingsong Mao, Jie Lu, Jialin Han, and Guangquan Zhang.  Multiobjective e-commerce recommendations based on hypergraph ranking.  Information Sciences, 471, 2019.
  * [208] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat Seng Chua.  Neural graph collaborative filtering.  SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 165–174, 7 2019.
  * [209] Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou, Fab-Rizio Silvestri, and Andreas Dami.  Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks.  WWW ’24 Companion, May 13â•fi17, 2024, Singapore, 1, 3 2024.
  * [210] Soheil Rezaee, Abolghasem Sadeghi-Niaraki, Maryam Shakeri, and Soo Mi Choi.  Personalized augmented reality based tourism system: Big data and user demographic contexts.  Applied Sciences (Switzerland), 11(13), 2021.
  * [211] Shakila Shaikh, Sheetal Rathi, and Prachi Janrao.  Recommendation system in E-Commerce Websites: A graph based approached.  In Proceedings - 7th IEEE International Advanced Computing Conference, IACC 2017, 2017.
  * [212] Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat Seng Chua.  Reinforced Negative Sampling over Knowledge Graph for Recommendation.  The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, pages 99–109, 4 2020.
  * [213] Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard De Melo, and Yongfeng Zhang.  Reinforcement knowledge graph reasoning for explainable recommendation.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [214] Yu Lei, Hongbin Pei, Hanqi Yan, and Wenjie Li.  Reinforcement Learning based Recommendation with Graph Convolutional Q-network.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [215] Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo.  RippleNet: Propagating user preferences on the knowledge graph for recommender systems.  In International Conference on Information and Knowledge Management, Proceedings, 2018.
  * [216] Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan.  Session-based recommendation with graph neural networks.  In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 2019.
  * [217] Chaoyang Wang, Zhiqiang Guo, Peng Pan, Jianjun Li, Guohui Li, Z Guo, J Li, G Li, and P Pan.  A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation.  ACM/IMS Transactions on Data Science (TDS), 2(4):1–25, 5 2022.
  * [218] Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, and Huajun Chen.  Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion.  In SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
  * [219] Tajuddeen Rabiu Gwadabe and Ying Liu.  Improving graph neural network for session-based recommendation system via non-sequential interactions.  Neurocomputing, 468:111–122, 1 2022.
  * [220] Zhenyan Ji, Mengdan Wu, Hong Yang, and José Enrique Armendáriz Íñigo.  Temporal sensitive heterogeneous graph neural network for news recommendation.  Future Generation Computer Systems, 125, 2021.
  * [221] Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat Seng Chua.  Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [222] Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin.  XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation.  IEEE Transactions on Knowledge and Data Engineering, 36(2), 2024.
  * [223] Sen Zhao, Wei Wei, Yifan Liu, Ziyang Wang, Wendi Li, Xian-Ling Mao, Shuai Zhu, Minghui Yang, and Zujie Wen.  Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning.  2023\.
  * [224] He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, and Jian Pei.  Trustworthy Graph Neural Networks: Aspects, Methods and Trends.  5 2022.
  * [225] Maral Kolahkaj, Ali Harounabadi, Alireza Nikravanshalmani, and Rahim Chinipardaz.  A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining.  Electronic Commerce Research and Applications, 42, 2020.
  * [226] Weihua Yuan, Hong Wang, Xiaomei Yu, Nan Liu, and Zhenghao Li.  Attention-based context-aware sequential recommendation model.  Information Sciences, 510, 2019.
  * [227] Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou.  Behavior sequence transformer for E-commerce recommendation in Alibaba.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [228] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang.  Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer.  In International Conference on Information and Knowledge Management, Proceedings, 2019.
  * [229] Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji Rong Wen, and Edward Y. Chang.  Improving sequential recommendation with knowledge-enhanced memory networks.  In 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, 2018.
  * [230] Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu, and Jimmy Huang.  KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [231] Balázs Hidasi and Alexandros Karatzoglou.  Recurrent neural networks with top-k gains for session-based recommendations.  In Proceedings of the 27th ACM international conference on information and knowledge management, pages 843–852, 2018.
  * [232] Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten de Rijke.  RepeatNet: A repeat aware neural recommendation machine for session-based recommendation.  In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 2019.
  * [233] Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power.  Semi-supervised sequence tagging with bidirectional language models.  In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), volume 1, 2017.
  * [234] Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet Orgun.  Sequential recommender systems: Challenges, progress and prospects.  In IJCAI International Joint Conference on Artificial Intelligence, volume 2019-August, 2019.
  * [235] Chen Wu and Ming Yan.  Session-aware information embedding for E-commerce product recommendation.  In International Conference on Information and Knowledge Management, Proceedings, volume Part F131841, 2017.
  * [236] Dietmar Jannach, Malte Ludewig, and Lukas Lerche.  Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts.  User Modeling and User-Adapted Interaction, 27(3-5), 2017.
  * [237] Tipajin Thaipisutikul and Ying Nong Chen.  An improved deep sequential model for context-aware POI recommendation.  Multimedia Tools and Applications, 83(1), 2024.
  * [238] Tipajin Thaipisutikul and Ying Nong Chen.  An improved deep sequential model for context-aware POI recommendation.  Multimedia Tools and Applications, 83(1), 2024.
  * [239] Yupeng Hou, Binbin Hu, Zhiqiang Zhang, and Wayne Xin Zhao.  CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space.  In SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
  * [240] Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, and Victor S. Sheng.  Frequency Enhanced Hybrid Attention Network for Sequential Recommendation.  In SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023.
  * [241] Jianyang Zhai, Xiawu Zheng, Chang Dong Wang, Hui Li, and Yonghong Tian.  Knowledge Prompt-tuning for Sequential Recommendation.  In MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, 2023.
  * [242] Jianyang Zhai, Xiawu Zheng, Chang Dong Wang, Hui Li, and Yonghong Tian.  Knowledge Prompt-tuning for Sequential Recommendation.  In MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, 2023.
  * [243] Jinpeng Wang, Ziyun Zeng, Yunxiao Wang, Yuting Wang, Xingyu Lu, Tianxiang Li, Jun Yuan, Rui Zhang, Hai Tao Zheng, and Shu Tao Xia.  MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation.  In MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, 2023.
  * [244] Wei Ji, Xiangyan Liu, An Zhang, Yinwei Wei, Yongxin Ni, and Xiang Wang.  Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation.  In MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, 2023.
  * [245] Jiaxi Tang and Ke Wang.  Personalized top-N sequential recommendation via convolutional sequence embedding.  In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining, volume 2018-Febuary, 2018.
  * [246] Balázs Hidasi and Alexandros Karatzoglou.  Recurrent Neural Net-works with Top-k Gains for Session-based Recommendations.  page 10, 2018.
  * [247] Gabriel De Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge.  Transformers4Rec: Bridging the Gap between NLP and sequential/session-based recommendation.  In RecSys 2021 - 15th ACM Conference on Recommender Systems, 2021.
  * [248] Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack.  SSE-PT: Sequential recommendation via personalized transformer.  In Proceedings of the 14th ACM conference on recommender systems, pages 328–337, 2020.
  * [249] Hengchang Hu, Wei Guo, Yong Liu, and Min Yen Kan.  Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems.  International Conference on Information and Knowledge Management, Proceedings, pages 843–853, 10 2023.
  * [250] Liwei Huang, Mingsheng Fu, Fan Li, Hong Qu, Yangjun Liu, and Wenyu Chen.  A deep reinforcement learning based long-term recommender system.  Knowledge-Based Systems, 213, 2021.
  * [251] Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie.  A Reinforcement Learning Framework for Explainable Recommendation.  In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2018-November, 2018.
  * [252] Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Dawei Yin, Yihong Zhao, and Jiliang Tang.  Deep Reinforcement Learning for List-wise Recommendations.  CEUR Workshop Proceedings, 1828, 2017.
  * [253] Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang.  Deep reinforcement learning for page-wise recommendations.  In RecSys 2018 - 12th ACM Conference on Recommender Systems, 2018.
  * [254] Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, and Xianzhi Wang.  Deep reinforcement learning in recommender systems: A survey and new perspectives.  Knowledge-Based Systems, 264, 2023.
  * [255] Hado Van Hasselt, Arthur Guez, and David Silver.  Deep reinforcement learning with double Q-Learning.  In 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016.
  * [256] Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath.  Deep reinforcement learning: A brief survey, 2017.
  * [257] Marios Kokkodis and Panagiotis G. Ipeirotis.  Demand-aware career path recommendations: A reinforcement learning approach.  Management Science, 67(7), 2021.
  * [258] Rong Gao, Haifeng Xia, Jing Li, Donghua Liu, Shuai Chen, and Gang Chun.  DRCGR: Deep reinforcement learning framework incorporating CNN and GAN-Based for interactive recommendation.  Proceedings - IEEE International Conference on Data Mining, ICDM, 2019-November:1048–1053, 11 2019.
  * [259] Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li.  DRN: A deep reinforcement learning framework for news recommendation.  In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, 2018.
  * [260] Yang Zhang, Chenwei Zhang, and Xiaozhong Liu.  Dynamic scholarly collaborator recommendation via competitive multi-agent reinforcement learning.  In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, 2017.
  * [261] Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, and Min Zhang.  EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems.  Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX), 1, 2 2024.
  * [262] Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, and Zhirong Wang.  Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication.  In RecSys 2020 - 14th ACM Conference on Recommender Systems, 2020.
  * [263] Krishna Sai Gottipati, Yashaswi Pathak, Rohan Nuttall, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E Taylor, and Sarath Chandar.  Maximum Reward Formulation In Reinforcement Learning.  Papers with Code, 2023.
  * [264] Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou, and Xuanjing Huang.  Mention recommendation in twitter with cooperative multi-agent reinforcement learning.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [265] Xueying Bai, Jian Guan, and Hongning Wang.  Model-based reinforcement learning with adversarial training for online recommendation.  In Advances in Neural Information Processing Systems, volume 32, 2019.
  * [266] Qihua Zhang, Junning Liu, Yuzhuo Dai, Yiyan Qi, Yifan Yuan, Kunlun Zheng, Fan Huang, and Xianfeng Tan.  Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022.
  * [267] Jia Wei Chang, Ching Yi Chiou, Jia Yi Liao, Ying Kai Hung, Chien Che Huang, Kuan Cheng Lin, and Ying Hung Pu.  Music recommender using deep embedding-based features and behavior-based reinforcement learning.  Multimedia Tools and Applications, 80(26-27), 2021.
  * [268] Zhiyuan Wang and Wenjie Wang.  Parameter design of grid-tied inverter using reinforcement learning.  IET Conference Proceedings, 2021(9):116–120, 2021.
  * [269] Binbin Hu, Chuan Shi, and Jian Liu.  Playlist recommendation based on reinforcement learning.  In IFIP Advances in Information and Communication Technology, volume 510, 2017.
  * [270] David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou.  RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising.  ACM Reference Format: RecSys, 18, 8 2018.
  * [271] Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, and Sungroh Yoon.  Reinforcement Learning based Recommender System using Biclustering Technique.  Computing Research Repository (CoRR), 1 2018.
  * [272] Wacharawan Intayoad, Chayapol Kamyod, and Punnarumol Temdee.  Reinforcement Learning for Online Learning Recommendation System.  In 6th Global Wireless Summit, GWS 2018, 2018.
  * [273] Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Morgane Lustman, Vince Gatto, Paul Covington, Jim McFadden, Tushar Chandra, and Craig Boutilier.  Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology.  arXiv, 5 2019.
  * [274] Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, and Yinghui Xu.  Reinforcement learning to rank in E-commerce search engine: Formalization, analysis, and application.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
  * [275] Jun Xu, Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, and Ji Rong Wen.  Reinforcement Learning to Rank with Pairwise Policy Gradient.  In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  * [276] Richard S. Sutton and Andrew G. Barto.  Reinforcement Learning, Second Edition: An Introduction - Complete Draft.  The MIT Press, 2018.
  * [277] Marco Wiering and Martijn van Otterlo.  Reinforcement Learning: State-of-the-Art, volume 12.  Springer Berlin, Heidelberg, 2012.
  * [278] Peng Jiang, Jiafeng Ma, and Jianming Zhang.  Deep Reinforcement Learning based Recommender System with State Representation.  In Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 2021.
  * [279] Peng Jiang, Jiafeng Ma, and Jianming Zhang.  Deep Reinforcement Learning based Recommender System with State Representation.  In Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 2021.
  * [280] Richard Liaw, Paige Bailey, Ying Li, Maria Dimakopoulou, and Yves Raimond.  REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale.  RecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems, 2022:684–685, 9 2022.
  * [281] Lu Wang, Xiaofeng He, Wei Zhang, and Hongyuan Zha.  Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
  * [282] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen.  A Survey of Large Language Models.  arXiv, 3 2023.
  * [283] Wang-cheng Kang, Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng.  Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction.  Papers with Code, 5 2023.
  * [284] Arpita Vats, Vinija Jain, Rahul Raja, and Aman Chadha.  Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review.  2 2024.
  * [285] Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic.  Galactica: A Large Language Model for Science.  11 2022.
  * [286] Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang.  GenRec: Large Language Model for Generative Recommendation.  arXiv, 7 2023.
  * [287] Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon.  Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences.  In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, 2023.
  * [288] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao.  Large Language Models are Zero-Shot Rankers for Recommender Systems.  pages 364–381, 5 2023.
  * [289] Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley.  Large Language Models as Zero-Shot Conversational Recommenders.  International Conference on Information and Knowledge Management, Proceedings, pages 720–730, 10 2023.
  * [290] Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, and Philip S. Yu.  LLMRec: Benchmarking Large Language Models on Recommendation Task.  8 2023.
  * [291] Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati.  On the Planning Abilities of Large Language Models : A Critical Investigation.  5 2023.
  * [292] Fan Yang, Amazon AI Alexa Seattle, Usa Zheng Chen, Ziyan Jiang, Usa Eunah Cho, Usa Xiaojiang Huang, Usa Yanbin Lu, Zheng Chen, Eunah Cho, and Xiaojiang Huang.  PALR: Personalization Aware LLMs for Recommendation.  arXiv, 5 2023.
  * [293] Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang.  RecMind: Large Language Model Powered Agent For Recommendation.  arXiv, 8 2024.
  * [294] Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen.  Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models.  arXiv, 5 2023.
  * [295] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He.  TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation.  In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, 2023.
  * [296] Viomesh Kumar Singh, Sangeeta Sabharwal, and Goldie Gabrani.  Comprehensive Analysis of Multimodal Recommender Systems.  In Data Intelligence and Cognitive Informatics, chapter Chapter 76, pages 887–901. Springer, Singapore, 2021.
  * [297] Xin Zhou.  MMRec: Simplifying Multimodal Recommendation.  ACM Multimedia Asia Workshops, 8 2023.
  * [298] Wei Wei, Chao Huang, Lianghao Xia, and Chuxu Zhang.  Multi-Modal Self-Supervised Learning for Recommendation.  In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, 2023.
  * [299] Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha.  Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [300] Wei Wei, Jiabin Tang, Yangqin Jiang, Lianghao Xia, Chao Huang, and Chao 2024 Huang.  PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning.  arXiv, 2 2024.
  * [301] Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, and Feijun Jiang.  Bootstrap Latent Representations for Multi-modal Recommendation.  In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, 2023.
  * [302] Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J Lim, and SK T-Brain.  Toward Multimodal Model-Agnostic Meta-Learning.  Papers with Code, 12 2018.
  * [303] Pushpendra Kumar and Ramjeevan Singh Thakur.  Recommendation system techniques and related issues: a survey.  International Journal of Information Technology, 10:495–501, 2018.
  * [304] Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, and Hui Liu.  Dear: Deep reinforcement learning for online advertising impression in recommender systems.  In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 750–758, 2021.
  * [305] Tarana Singh, Anand Nayyar, and Arun Solanki.  Multilingual opinion mining movie recommendation system using rnn.  In Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019), pages 589–605. Springer, 2020.
  * [306] Ruslan Salakhutdinov and Andriy Mnih.  Bayesian probabilistic matrix factorization using markov chain Monte Carlo.  In Proceedings of the 25th International Conference on Machine Learning, 2008.
  * [307] Francisco J. Peña, DIarmuid O’Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, and Aonghus Lawlor.  Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation.  In RecSys 2020 - 14th ACM Conference on Recommender Systems, 2020.
  * [308] Ruining He, Wang Cheng Kang, and Julian McAuley.  Translation-based recommendation.  RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, pages 161–169, 8 2017.
  * [309] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko.  Translating embeddings for modeling multi-relational data.  In Advances in Neural Information Processing Systems, 2013.
  * [310] Aditya Grover and Jure Leskovec.  Node2vec: Scalable feature learning for networks.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume 13-17-August-2016, 2016.
  * [311] Deqing Yang, Zikai Guo, Ziyi Wang, Juyang Jiang, Yanghua Xiao, and Wei Wang.  A Knowledge-Enhanced Deep Recommendation Framework Incorporating GAN-Based Models.  In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2018-November, 2018.
  * [312] Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu.  SHINE: Signed heterogeneous information network embedding for sentiment link prediction.  In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining, volume 2018-Febuary, 2018.
  * [313] Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu.  Leveraging meta-path based context for top-n recommendation with a neural co-attention model.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
  * [314] Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han.  Personalized entity recommendation: A heterogeneous information network approach.  In WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 2014.
  * [315] Chen Luo, Wei Pang, Zhe Wang, and Chenghua Lin.  Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations.  In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2015-January, 2014.
  * [316] Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue, and Bin Wu.  Semantic path based personalized recommendation on weighted heterogeneous information networks.  In International Conference on Information and Knowledge Management, Proceedings, volume 19-23-Oct-2015, 2015.
  * [317] Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose.  Relational collaborative filtering: Modeling multiple item relations for recommendation.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [318] Xiaoli Tang, Tengyun Wang, Haizhi Yang, and Hengjie Song.  Akupm: Attention-enhanced knowledge-aware user preference model for recommendation.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [319] Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, and Jian Tang.  An End-to-end neighborhood-based interaction model for knowledge-enhanced recommendation.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [320] Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, and Jingsong Yu.  Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion.  In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, pages 1006–1014, New York, NY, USA, 2020. Association for Computing Machinery.
  * [321] Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang.  Personalized News Recommendation with Knowledge-aware Interactive Matching.  In SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.
  * [322] Armin Toroghi, Griffin Floto, Zhenwei Tang, and Scott Sanner.  Bayesian Knowledge-driven Critiquing with Indirect Evidence.  In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’23, pages 1838–1842, New York, NY, USA, 2023. Association for Computing Machinery.
  * [323] Yangqin Jiang, Yuhao Yang, Lianghao Xia, and Chao Huang.  DiffKG: Knowledge Graph Diffusion Model for Recommendation.  In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM ’24, pages 313–321, New York, NY, USA, 2024. Association for Computing Machinery.
  * [324] Mahesan Niranjan.  On-Line Q-Learning Using Connectionist Systems.  1994\.
  * [325] Tariq Mahmood, Ghulam Mujtaba, and Adriano Venturini.  Dynamic personalization in conversational recommender systems.  Information Systems and e-Business Management, 12(2), 2014.
  * [326] Cameron B. Browne, Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton.  A survey of Monte Carlo tree search methods, 2012.
  * [327] Chengrun Qiu, Yang Hu, Yan Chen, and Bing Zeng.  Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications.  IEEE Internet of Things Journal, 6(5), 2019.
  * [328] Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine.  Soft Actor-Critic Algorithms and Applications.  Paper with Code, 2018.
  * [329] Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, and Qing He.  Policy gradients for contextual recommendations.  In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019.
  * [330] Don X. Sun and Frederick Jelinek.  Statistical Methods for Speech Recognition.  Journal of the American Statistical Association, 94(446), 1999.
  * [331] Ronald Rosenfeld.  Two decdes of statistical language modeling where do we go form here? Where do we go from here?  Proceedings of the IEEE, 88(8):1270–1275, 2000.
  * [332] Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin.  A Neural Probabilistic Language Model.  In Journal of Machine Learning Research, volume 3, 2003.
  * [333] Tomaš Mikolov, Martin Karafiát, Lukaš Burget, Cernocky Jan, and Sanjeev Khudanpur.  Recurrent neural network based language model.  In Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010, 2010.
  * [334] Stefan Kombrink, Tomáš Mikolov, Martin Karafiát, and Lukáš Burget.  Recurrent neural network based language modeling in meeting recognition.  In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2011.
  * [335] Jacob Devlin, Ming Wei Chang, Kenton Lee, and Kristina Toutanova.  BERT: Pre-training of deep bidirectional transformers for language understanding.  In NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, volume 1, 2019.
  * [336] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei.  Scaling Instruction-Finetuned Language Models.  10 2022.
  * [337] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  In Advances in neural information processing systems, pages 5998–6008, 2017.
  * [338] Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, and Jimmy Xiangji Huang.  A systematic study and comprehensive evaluation of chatgpt on benchmark datasets.  arXiv preprint arXiv:2305.18486, 2023.
  * [339] Aashiq Muhamed, Iman Keivanloo, Sujan Perera, James Mracek, Yi Xu, Qingjun Cui, Santosh Rajagopalan, Belinda Zeng, and Trishul Chilimbi.  CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models.  arXiv, 2021.
  * [340] Bowen Yang, Cong Han, Yu Li, Lei Zuo, and Zhou Yu.  Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information.  In Findings of the Association for Computational Linguistics: NAACL 2022 - Findings, 2022.
  * [341] Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan.  U-BERT: Pre-training User Representations for Improved Recommendation.  In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, volume 5B, 2021.
  * [342] Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang.  UserBERT: Pre-training User Model with Contrastive Self-supervision.  In SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
  * [343] Yingrui Yang, Yifan Qiao, Jinjin Shao, Xifeng Yan, and Tao Yang.  Lightweight composite re-ranking for efficient keyword search with BERT.  In WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining, 2022.
  * [344] Song Zhang, Nan Zheng, and Danli Wang.  GBERT: Pre-training User representations for Ephemeral Group Recommendation.  In International Conference on Information and Knowledge Management, Proceedings, 2022.
  * [345] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang.  Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt and Predict Paradigm (P5).  In RecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems, 2022.
  * [346] Zizhuo Zhang and Bang Wang.  Prompt Learning for News Recommendation.  In SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023.
  * [347] Xuyang Wu, Alessandro Magnani, Suthee Chaidaroon, Ajit Puthenputhussery, Ciya Liao, and Yi Fang.  A Multi-task Learning Framework for Product Ranking with BERT.  In WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022.
  * [348] Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P Dickerson, and Chirag Shah.  RecRec: Algorithmic Recourse for Recommender Systems.  In CIKM ’23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, number 23, 2023.
  * [349] Ruining He and Julian McAuley.  VBPR: Visual Bayesian personalized ranking from implicit feedback.  In 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016.
  * [350] Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat Seng Chua.  Attentive collaborative filtering: Multimedia recommendation with item-And component-level attention.  In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017.
  * [351] Guangneng Hu, Yu Zhang, and Qiang Yang.  Conet: Collaborative cross networks for cross-domain recommendation.  In International Conference on Information and Knowledge Management, Proceedings, 2018.
  * [352] Gediminas Adomavicius, Konstantin Bauman, Alexander Tuzhilin, and Moshe Unger.  Context-Aware Recommender Systems: From Foundations to Recent Developments.  In Recommender Systems Handbook: Third Edition, chapter Chapter 5, pages 211–250. Springer, New York, NY, 2022.
  * [353] Steffen Rendle.  Factorization machines.  Proceedings - IEEE International Conference on Data Mining, ICDM, pages 995–1000, 2010.
  * [354] Yuchin Juan, Yong Zhuang, Wei Sheng Chin, and Chih Jen Lin.  Field-aware factorization machines for CTR prediction.  In RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, 2016.
  * [355] Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo, and China Jinmm.  Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation Guangdong Key Laboratory of Big Data Analysis and Processing.  In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)., 2018.
  * [356] Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat Seng Chua.  Attentional factorization machines: Learning the weight of feature interactions via attention networks.  In IJCAI International Joint Conference on Artificial Intelligence, volume 0, 2017.
  * [357] Angela Carrera-Rivera, Felix Larrinaga, and Ganix Lasa.  Context-awareness for the design of Smart-product service systems: Literature review, 2022.
  * [358] Emrul Hasan, Mizanur Rahman, Chen Ding, Jimmy Xiangji Huang, and Shaina Raza.  based recommender systems: A survey of approaches, challenges and future perspectives.  arXiv preprint arXiv:2405.05562, 2024.
  * [359] Julian McAuley and Jure Leskovec.  Hidden factors and hidden topics: Understanding rating dimensions with review text.  In RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems, 2013.
  * [360] Yunzhi Tan, Min Zhang, Yiqun Liu, and Shaoping Ma.  Rating-boosted latent topics: Understanding users and items with ratings and reviews.  In IJCAI International Joint Conference on Artificial Intelligence, volume 2016-January, 2016.
  * [361] Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu.  Convolutional matrix factorization for document context-aware recommendation.  RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, pages 233–240, 9 2016.
  * [362] Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang.  SentiRec: Sentiment Diversity-aware Neural News Recommendation.  pages 44–53.
  * [363] Jing Liu, Jinbao Song, Chen Li, Xiaoya Zhu, and Ruyi Deng.  A Hybrid News Recommendation Algorithm Based on K-means Clustering and Collaborative Filtering.  In Journal of Physics: Conference Series, volume 1881, 2021.
  * [364] Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan Kankanhalli.  A3NCF: An Adaptive Aspect Attention Model for Rating Prediction.  2018\.
  * [365] Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, and Tat-Seng Chua.  Attentive Aspect Modeling for Review-aware Recommendation.  ACM Transactions on Information Systems, 37(3):28, 11 2018.
  * [366] Songyin Luo, Xiangkui Lu, Jun Wu, and Jianbo Yuan.  Aware neural recommendation with cross-modality mutual attention.  In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 3293–3297, 2021.
  * [367] Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu.  Interpretable convolutional neural networks with dual local and global attention for review rating prediction.  In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, 2017.
  * [368] Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma.  Neural attentional rating regression with review-level explanations.  In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, 2018.
  * [369] Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, and Xing Xie.  NRPA: Neural Recommendation with Personalized Attention.  SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 19:1233–1236, 5 2019.
  * [370] Yichao Lu, Ruihai Dong, and Barry Smyth.  Coevolutionary recommendation model: Mutual learning between ratings and reviews.  In Proceedings of the 2018 World Wide Web Conference, pages 773–782, 2018.
  * [371] Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, and Gerard De Melo.  Asymmetrical hierarchical networks with attentive interactions for interpretable review-based recommendation.  In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 7667–7674, 2020.
  * [372] Ke Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, and Chunyang Wang.  Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation.  ACM Transactions on Knowledge Discovery from Data, 17(1), 2023.
  * [373] Rose Catherine and William Cohen.  TransNets: Learning to transform for recommendation.  In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, 2017.
  * [374] Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao.  DAML: Dual attention mutual learning between ratings and reviews for item recommendation.  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 344–352, 7 2019.
  * [375] Peng Liu, Lemei Zhang, and Jon Atle Gulla.  Multilingual Review-Aware Deep Recommender System via Aspect-based Sentiment Analysis.  2020\.
  * [376] Jin Yao Chin, Shafiq Joty, Kaiqi Zhao, and Gao Cong.  ANR: Aspect-based Neural Recommender.  In International Conference on Information and Knowledge Management, Proceedings, 2018.
  * [377] Konstantin Bauman, Bing Liu, and Alexander Tuzhilin.  Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Part F129685, 2017.
  * [378] Cataldo Musto, Marco De Gemmis, Giovanni Semeraro, and Pasquale Lops.  A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews.  In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, 2017.
  * [379] Liliya Volkova, Elena Yagunova, Ekaterina Pronoza, Alexandra Maslennikova, Danil Bliznuk, Margarita Tokareva, and Ali Abdullaev.  Recommender System for Tourist Itineraries Based on Aspects Extraction from Reviews Corpora.  Polibits, 57, 2018.
  * [380] Aminu Da’u, Naomie Salim, Idris Rabiu, and Akram Osman.  Weighted aspect-based opinion mining using deep learning for recommender system.  Expert Systems with Applications, 140, 2020.
  * [381] Aminu Da’u, Naomie Salim, Idris Rabiu, and Akram Osman.  Recommendation system exploiting aspect-based opinion mining with deep learning method.  Information Sciences, 512, 2020.
  * [382] Ahlem Drif, Sami Guembour, and Hocine Cherifi.  A Sentiment Enhanced Deep Collaborative Filtering Recommender System.  In Studies in Computational Intelligence, volume 944, 2021.
  * [383] Avinash Bhojwani, Vanshika Jolly, Saksham Goel, and M. Anand Kumar.  Aspect Based Neural Recommender Using Adaptive Prediction.  In 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2023, 2023.
  * [384] Hanning Yuan, Zhengyu Chen, Jingting Yang, Shuliang Wang, Jing Geng, and Chuwen Ke.  A Hybrid Aspect Based Latent Factor Model for Recommendation.  Chinese Journal of Electronics, 29(3), 2020.
  * [385] Kexin Yin, Xiao Fang, Bintong Chen, and Olivia R.Liu Sheng.  Diversity Preference-Aware Link Recommendation for Online Social Networks.  Information Systems Research, 34(4), 2023.
  * [386] Payal Mehra.  Unexpected surprise: Emotion analysis and aspect based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists.  Tourism Management Perspectives, 45:101063, 1 2023.
  * [387] Hengyun Li, Bruce X.B. Yu, Gang Li, and Huicai Gao.  Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews.  Tourism Management, 96:104707, 6 2023.
  * [388] Shoujin Wang, Xiuzhen Zhang, Yan Wang, and Francesco Ricci.  Trustworthy recommender systems.  ACM Transactions on Intelligent Systems and Technology, 2022.
  * [389] Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma.  Explicit Factor Models for explainable recommendation based on phrase-level sentiment analysis.  In SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014.
  * [390] Xiangnan He, Tao Chen, Min Yen Kan, and Xiao Chen.  TriRank: Review-aware explainable recommendation by modeling aspects.  In International Conference on Information and Knowledge Management, Proceedings, volume 19-23-Oct-2015, 2015.
  * [391] Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, and Tat Seng Chua.  TEM: Tree-enhanced embedding model for explainable recommendation.  In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, 2018.
  * [392] Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang.  Learning heterogeneous knowledge base embeddings for explainable recommendation.  Algorithms, 11(9), 2018.
  * [393] Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, and Mohan Kankanhalli.  MMalfM: Explainable recommendation by leveraging reviews and images.  ACM Transactions on Information Systems, 37(2), 2019.
  * [394] Krisztian Balog, Filip Radlinski, and Shushan Arakelyan.  Transparent, scrutable and explainable user models for personalized recommendation.  In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
  * [395] Lei Li, Yongfeng Zhang, and Li Chen.  Personalized transformer for explainable recommendation.  In ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021.
  * [396] Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang.  Counterfactual Explainable Recommendation.  In International Conference on Information and Knowledge Management, Proceedings, 2021.
  * [397] Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, and Yongfeng Zhang.  Explainable Fairness in Recommendation.  In SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
  * [398] Lei Li, Yongfeng Zhang, and Li Chen.  Personalized prompt learning for explainable recommendation.  ACM Transactions on Information Systems, 41(4):1–26, 2023.
  * [399] Tianjun Wei, Tommy WS Chow, Jianghong Ma, and Mingbo Zhao.  Expgcn: Review-aware graph convolution network for explainable recommendation.  Neural Networks, 157:202–215, 2023.
  * [400] Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, and Casey Fiesler.  Fairness and transparency in recommendation: The users’ perspective.  In UMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 2021.
  * [401] Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma.  A Survey on the Fairness of Recommender Systems.  ACM Transactions on Information Systems, 41(3), 2023.
  * [402] Bashir Rastegarpanah, Krishna P. Gummadi, and Mark Crovella.  Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems.  In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, 2019.
  * [403] Sirui Yao and Bert Huang.  Beyond parity: Fairness objectives for collaborative filtering.  In Advances in Neural Information Processing Systems, volume 2017-December, 2017.
  * [404] Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma.  Recommendation Independence.  In Proceedings of Machine Learning Research, volume 81, 2018.
  * [405] Ziwei Zhu, Xia Hu, and James Caverlee.  Fairness-aware tensor-based recommendation.  In International Conference on Information and Knowledge Management, Proceedings, 2018.
  * [406] Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow.  Fairness in recommendation ranking through pairwise comparisons.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [407] Mengting Wan, Jianmo Ni, Rishabh Misra, and Julian McAuley.  Addressing marketing bias in product recommendations.  In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, 2020.
  * [408] Shaina Raza and Chen Ding.  A Regularized Model to Trade-off between Accuracy and Diversity in a News Recommender System.  In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 2020.
  * [409] Shaina Raza.  Bias Reduction News Recommendation System.  Digital 2024, Vol. 4, Pages 92-103, 4(1):92–103, 12 2023.
  * [410] Ziwei Zhu, Jianling Wang, and James Caverlee.  Fairness-aware personalized ranking recommendation via adversarial learning.  arXiv preprint arXiv:2103.07849, 2021.
  * [411] Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang.  Towards Personalized Fairness based on Causal Notion.  In SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.
  * [412] Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang.  Towards Long-term Fairness in Recommendation.  In WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021.
  * [413] Rodrigo Borges and Kostas Stefanidis.  Enhancing long term fairness in recommendations with variational autoencoders.  In 11th International Conference on Management of Digital EcoSystems, MEDES 2019, 2019.
  * [414] Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, and James Foulds.  Debiasing career recommendations with neural fair collaborative filtering.  In The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, 2021.
  * [415] Yangkun Li, Mohamed Laid Hedia, Weizhi Ma, Hongyu Lu, Min Zhang, Yiqun Liu, and Shaoping Ma.  Contextualized Fairness for Recommender Systems in Premium Scenarios.  Big Data Research, 27, 2022.
  * [416] Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates.  FAIR: A fair top-k ranking algorithm.  In International Conference on Information and Knowledge Management, Proceedings, volume Part F131841, 2017.
  * [417] Harald Steck.  Calibrated recommendations.  In RecSys 2018 - 12th ACM Conference on Recommender Systems, 2018.
  * [418] Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi.  Fairness-aware ranking in search & recommendation systems with application to linkedin talent search.  In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  * [419] Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, and Shengyu Zhang.  Personalized fairness-aware re-ranking for microlending.  In RecSys 2019 - 13th ACM Conference on Recommender Systems, 2019.
  * [420] Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, and Shaoping Ma.  Fairness-aware group recommendation with pareto-efficiency.  In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, 2017.
  * [421] Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee.  Fairness among New Items in Cold Start Recommender Systems.  In SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.
  * [422] Di Jin, Luzhi Wang, He Zhang, Yizhen Zheng, Weiping Ding, Feng Xia, and Shirui Pan.  A survey on fairness-aware recommender systems.  Information Fusion, 100:101906, 12 2023.
  * [423] Yao Wu, Jian Cao, and Guandong Xu.  Fairness in Recommender Systems: Evaluation Approaches and Assurance Strategies.  ACM Transactions on Knowledge Discovery from Data, 18(1), 8 2023.
  * [424] Sriharsha Dara, C. Ravindranath Chowdary, and Chintoo Kumar.  A survey on group recommender systems.  Journal of Intelligent Information Systems, 54(2), 2020.
  * [425] Xuan Bi, Annie Qu, Junhui Wang, and Xiaotong Shen.  A Group-Specific Recommender System.  Journal of the American Statistical Association, 112(519), 2017.
  * [426] Nguyen Thanh Toan, Phan Thanh Cong, Nguyen Thanh Tam, Nguyen Quoc Viet Hung, and Bela Stantic.  Diversifying Group Recommendation.  IEEE Access, 6, 2018.
  * [427] Nicola Capuano, Francisco Chiclana, Enrique Herrera-Viedma, Hamido Fujita, and Vincenzo Loia.  Fuzzy Group Decision Making for influence-aware recommendations.  Computers in Human Behavior, 101, 2019.
  * [428] Zhiwei Guo, Wenru Zeng, Heng Wang, and Yu Shen.  An Enhanced Group Recommender System by Exploiting Preference Relation.  IEEE Access, 7, 2019.
  * [429] Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou.  Social influence-based group representation learning for group recommendation.  In Proceedings - International Conference on Data Engineering, volume 2019-April, 2019.
  * [430] Pablo Sánchez and Alejandro Bellogín.  On the effects of aggregation strategies for different groups of users in venue recommendation.  Information Processing and Management, 58(5), 2021.
  * [431] Firat Ismailoglu.  Aggregating user preferences in group recommender systems: A crowdsourcing approach.  Decision Support Systems, 152, 2022.
  * [432] Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato.  Multistakeholder recommendation: Survey and research directions.  User Modeling and User-Adapted Interaction, 30(1), 2020.
  * [433] Anitha Anandhan, Liyana Shuib, Maizatul Akmar Ismail, and Ghulam Mujtaba.  Social Media Recommender Systems: Review and Open Research Issues.  IEEE Access, 6, 2018.
  * [434] Jyoti Shokeen and Chhavi Rana.  A study on features of social recommender systems.  Artificial Intelligence Review, 53(2), 2020.
  * [435] Amazon’s Product Recommendation System In 2021: How Does The Algorithm Of The eCommerce Giant Work? - Recostream.
  * [436] Yan Guo, Minxi Wang, and Xin Li.  Application of an improved Apriori algorithm in a mobile e-commerce recommendation system.  Industrial Management and Data Systems, 117(2), 2017.
  * [437] Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, and Dawei Yin.  Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems.  In International Conference on Information and Knowledge Management, Proceedings, 2020.
  * [438] Carlos A. Gomez-Uribe and Neil Hunt.  The netflix recommender system: Algorithms, business value, and innovation.  ACM Transactions on Management Information Systems, 6(4), 2015.
  * [439] Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, and Justin Basilico.  Deep learning for recommender systems: A Netflix case study.  AI Magazine, 42(3), 2021.
  * [440] Chhavi Maheshwari.  Music Recommendation on Spotify using Deep Learning.  12 2023.
  * [441] Kurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, and Romain Yon.  Music Personalization at Spotify.  In RecSys ’16: Proceedings of the 10th ACM Conference on Recommender Systems, pages 373–373. Association for Computing Machinery (ACM), 9 2016.
  * [442] Germán Cheuque, José Guzmán, and Denis Parra.  Recommender systems for online video game platforms: The case of steam.  In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 2019.
  * [443] Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner.  Towards health (Aware) recommender systems.  In ACM International Conference Proceeding Series, volume Part F128634, 2017.
  * [444] Juan G.Diaz Ochoa, Orsolya Csiszár, and Thomas Schimper.  Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.  BMC Medical Informatics and Decision Making, 21(1), 2021.
  * [445] Deepika Sharma, Gagangeet Singh Aujla, and Rohit Bajaj.  Evolution from ancient medication to human-centered Healthcare 4.0: A review on health care recommender systems.  International Journal of Communication Systems, 36(12), 2023.
  * [446] Uzair Aslam Bhatti, Mengxing Huang, Di Wu, Yu Zhang, Anum Mehmood, and Huirui Han.  Recommendation system using feature extraction and pattern recognition in clinical care systems.  Enterprise Information Systems, 13(3), 2019.
  * [447] André Calero Valdez, Martina Ziefle, Katrien Verbert, Alexander Felfernig, and Andreas Holzinger.  Recommender systems for health informatics: State-of-the-art and future perspectives.  In Machine Learning for Health Informatics, chapter Chapter 21, pages 391–414. Springer, Cham, 2016.