Abstract
Has it ever happened to you that services like Spotify, Netflix or YouTube showed you recommendations on the same topic over and over again? This might be caused by the lack of serendipity in recommender systems of these services. Recommender systems are software tools that suggest items, such as audio recordings or videos, of interest to users. Meanwhile, serendipity is the property of these systems, which indicates the degree, to which they suggest items that pleasantly surprise users. In this talk, I will provide an overview of serendipity in recommender systems. In particular, I will talk about how the concept of serendipity has been defined and measured in recommender systems, and what experiments have been conducted to investigate this concept. I will also touch on recommendation algorithms designed to suggest serendipitous items and discuss future directions of the topic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, L., Yang, Y., Wang, N., Yang, K., Yuan, Q.: How serendipity improves user satisfaction with recommendations? a large-scale user evaluation. In: The World Wide Web Conference, pp. 240–250 (2019)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of RecSys 2010, pp. 39–46. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1864708.1864721
Fu, Z., Niu, X., Maher, M.L.: Deep learning models for serendipity recommendations: a survey and new perspectives. ACM Comput. Surv. 56(1), 1–26 (2023)
Fu, Z., Niu, X., Yu, L.: Wisdom of crowds and fine-grained learning for serendipity recommendations. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 739–748 (2023)
Kotkov, D., Konstan, J.A., Zhao, Q., Veijalainen, J.: Investigating serendipity in recommender systems based on real user feedback. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 1341–1350 (2018)
Kotkov, D., Maslov, A., Neovius, M.: Revisiting the tag relevance prediction problem. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1768–1772. SIGIR 2021, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3404835.3463019
Kotkov, D., Medlar, A., Glowacka, D.: Rethinking serendipity in recommender systems. In: ACM SIGIR Conference on Human Information Interaction and Retrieval. CHIIR 2023, Association for Computing Machinery, New York, NY, USA (2023)
Kotkov, D., Medlar, A., Liu, Y., Glowacka, D.: On the negative perception of cross-domain recommendations and explanations. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2024. Association for Computing Machinery, New York, USA (2024). https://doi.org/10.1145/3626772.3657735
Kotkov, D., Medlar, A., Maslov, A., Satyal, U.R., Neovius, M., Glowacka, D.: The tag genome dataset for books. In: ACM SIGIR Conference on Human Information Interaction and Retrieval, pp. 353–357. CHIIR 2022, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3498366.3505833
Kotkov, D., Medlar, A., Triin, K., Glowacka, D.: The dark matter of serendipity in recommender systems. In: Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval. CHIIR 2024, Association for Computing Machinery, New York, NY, USA (2024). https://doi.org/10.1145/3627508.3638342
Kotkov, D., Veijalainen, J., Wang, S.: A serendipity-oriented greedy algorithm for recommendations. In: Proceedings of the 13th International Conference on Web Information systems and Technologies. SCITEPRESS (2017)
Kotkov, D., Wang, S., Veijalainen, J.: Improving serendipity and accuracy in cross-domain recommender systems. In: Monfort, V., Krempels, K.-H., Majchrzak, T.A., Traverso, P. (eds.) WEBIST 2016. LNBIP, vol. 292, pp. 105–119. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66468-2_6
Kotkov, D., Zhao, Q., Launis, K., Neovius, M.: Clusterexplorer: enable user control over related recommendations via collaborative filtering and clustering. In: Proceedings of the 2020 ACM conference on Recommender systems (2020)
Pandey, G., Kotkov, D., Semenov, A.: Recommending serendipitous items using transfer learning. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1771–1774 (2018)
Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, chap. Introduction to Recommender Systems Handbook, pp. 1–35. Springer US (2011)
Smets, A., Vannieuwenhuyze, J., Ballon, P.: Serendipity in the city: user evaluations of urban recommender systems. J. Am. Soc. Inf. Sci. 73(1), 19–30 (2022)
Trattner, C., Oberegger, A., Marinho, L., Parra, D.: Investigating the utility of the weather context for point of interest recommendations. Inf. Technol. Tourism 19, 117–150 (2018)
Vig, J., Sen, S., Riedl, J.: The tag genome: encoding community knowledge to support novel interaction. ACM Trans. Interact. Intell. Syst. 2(3), 13:1–13:44 (2012). https://doi.org/10.1145/2362394.2362395
Wang, N., Chen, L.: How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis. User Model. User-Adap. Interact. 33, 1–39 (2022)
Wang, N., Chen, L., Yang, Y.: The impacts of item features and user characteristics on users’ perceived serendipity of recommendations. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 266–274 (2020)
Zheng, Q., Chan, C.-K., Ip, H.H.S.: An unexpectedness-augmented utility model for making serendipitous recommendation. In: Perner, P. (ed.) ICDM 2015. LNCS (LNAI), vol. 9165, pp. 216–230. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20910-4_16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kotkov, D. (2024). Overview of Serendipity in Recommender Systems. In: Stefanidis, K., Systä, K., Matera, M., Heil, S., Kondylakis, H., Quintarelli, E. (eds) Web Engineering. ICWE 2024. Lecture Notes in Computer Science, vol 14629. Springer, Cham. https://doi.org/10.1007/978-3-031-62362-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-62362-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62361-5
Online ISBN: 978-3-031-62362-2
eBook Packages: Computer ScienceComputer Science (R0)