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Cited by 30

The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

Fu, Zhe, Niu, Xi, Maher, Mary Lou·2024·ACM Computing Surveys
DOI 10.1145/3605145
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Abstract

Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain.

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Cites

Show all 88 references

Research works printed but not linked: 22

  1. A. G. Borselli. 1965. Serendipity and the three princes: From the Peregrinaggio of 1557 Theodore G. Remer (Ed.). University of Oklahoma Press Norman OK. (The compilation of the work here is regarded as probably by Michele Tramezzino and translated by Augusto G. Borselli and Theresa L. Borselli.)
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  5. Ludovik Coba, Panagiotis Symeonidis, and Markus Zanker. 2018. Novelty-aware matrix factorization based on items’ popularity. In Proceedings of the International Conference of the Italian Association for Artificial Intelligence. 516–527.
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  7. Xi Niu, Wlodek Zadrozny, Kazjon Grace, and Weimao Ke. 2018. Computational surprise in information retrieval. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 1427–1429.
  8. Marwa Hussien Mohamed, Mohamed Helmy Khafagy, Heba Elbeh, and Ahmed Mohamed Abdalla. 2019. Sparsity and cold start recommendation system challenges solved by hybrid feedback. International Journal of Engineering Research and Technology 12, 12 (2019), 2735–2742.
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  12. Kazjon Grace, Mary Lou Maher, Nicholas Davis, and Omar Eltayeby. 2018. Surprise walks: Encouraging users towards novel concepts with sequential suggestions. In Proceedings of the 9th International Conference on Computational Creativity (ICCC’18).
  13. Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. 2020. Directional and explainable serendipity recommendation. In Proceedings of The Web Conference 2020. 122–132.
  14. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Łukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS’17) .
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  16. Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 2018 World Wide Web Conference. 167–176.
  17. Rupert G. Miller Jr. 2011. Survival Analysis. John Wiley & Sons.
  18. Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS’19).
  19. James Bennett and Stan Lanning. 2007. The Netflix Prize. In Proceedings of the KDD Cup and Workshop, Vol. 2007. ACM, New York, NY, 1–4.
  20. Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, and Seung-Taek Park. 2011. Novel recommendation based on personal popularity tendency. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining. IEEE, Los Alamitos, CA, 507–516.
  21. Yong Wang, Jingyuan Wang, and Li Li. 2018. Enhancing long tail recommendation based on user’s experience evolution. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD’18). IEEE, Los Alamitos, CA, 25–30.
  22. Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, and Zi Huang. 2019. From zero-shot learning to cold-start recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4189–4196.

Metadata

Authors
Fu, Z., Niu, X., & Maher, M. L.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2023-08-26
Volume
56
Issue
1
Pages
1-26
ISSN
  • 0360-0300
  • 1557-7341
DOI
10.1145/3605145
Cited by
30
References
88
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
National Science Foundation (1910696)

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CoChat. (2026). Citation check: Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives. https://app.cochat.ai/papers/10.1145/3605145
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Fu, Z., Niu, X., & Maher, M. L. (2024). Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives. ACM Computing Surveys. https://doi.org/10.1145/3605145

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