Abstract
Information recommendation systems are used to provide suitable information to users. Conventional systems entail the difficulty that once an item is selected, only similar items are recommended. This tendency leads to user “boredom” with the recommendations and markedly reduces the recommendation effectiveness. Therefore, the present study focuses on “serendipity”, an indicator such as unexpectedness in information recommendation, to eliminate user boredom. Novelty and diversity are used to evaluate serendipity, and the unique preferences of the target user, which have not been considered in previous research, are taken into account. The conventional method, existing method from previous studies and six proposed methods are then compared. The findings indicate that the best results were obtained by recommending items that other users did not like but which had a high predicted evaluation value. This method resulted in all recommended items having novelty, and the diversity was also improved by about 14% compared to the conventional method, and serendipity was improved the most. Furthermore, it maintained the same recommendation accuracy as the conventional method. This method is expected to improve user satisfaction.
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References
Hawkins, D.T., Levy, L.R., Montgomery, K.L.: Knowledge gateways: the building blocks. Inf. Process. Manag. 24(4), 459–468 (1988)
Bates, M.J.: The design of browsing and berrypicking techniques for the online search interface. Online Rev. 13(5), 407–424 (1989)
Roy, D., Dutta, M.: A systematic review and research perspective on recommender systems. J. Big Data 9, 59 (2022)
Mu, R., Zeng, X., Han, L.: A survey of recommender systems based on deep learning. IEEE Access 6, 69009–69022 (2018)
Najmani, K., Benlahmar, E.H., Sael, N., Zellou, A.: Collaborative filtering approach: a review of recent research. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds.) AI2SD 2020. AISC, vol. 1418, pp. 151–163. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-90639-9_13
Lops, P., Jannach, D., Musto, C., Bogers, T., Koolen, M.: Trends in content-based recommendation. User Model. User-Adap. Inter. 29(2), 239–249 (2019)
Figà Talamanca, G., Arfini, S.: Through the newsfeed glass: rethinking filter bubbles and echo chambers. Philos. Technol. 35, 20 (2022)
Qazi, M.A., et al.: Filter bubbles in recommender systems: fact or fallacy–a systematic review. WIREs Data Min. Knowl. Disc. 13(6), e1512 (2023)
Copeland, S., Ross, W., Sand, M.: Introduction–a science of serendipity? In: Copeland, S., Ross, W., Sand, M. (eds.) Serendipity Science: An Emerging Field and its Methods, pp. 1–16. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-33529-7_1
Ping, Y., Li, Y., Zhu, J.: Beyond accuracy measures: the effect of diversity, novelty, and serendipity in recommender systems on user engagement. Electron. Commer. Res. (2024)
Karimi, S., Rahmani, H.A., Naghiaei, M., Safari, L.: Provider fairness and beyond-accuracy trade-offs in recommender systems. In: 6th FAccTRec Workshop: Responsible Recommendation, RecSys 2023, arXiv preprint arXiv:2309.04250 (2023)
Domoto, H., Uchiya, T., Takumi, I.: Development of a hybrid information recommendation system considering serendipity. In: 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE 2023), pp. 1065–1066. IEEE, Kyoto (2023)
Ren, W., Wang, L., Liu, K., Guo, R., Lim, E. P., Fu, Y.: Mitigating popularity bias in recommendation with unbalanced interactions: a gradient perspective. In: Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM), pp. 1198–1203. IEEE, USA (2022)
Roberts, R.M.: Serendipity: Accidental Discoveries in Science, 2nd edn. John Wiley & Sons Inc., New York (1989)
MovieLens Dataset. https://grouplens.org/datasets/movielens/. Accessed 21 June 2024
Antiopi, P., Boutsinas, B.: Addressing the cold-start problem in recommender systems based on frequent patterns. Algorithms 16(4), 182 (2023). https://doi.org/10.3390/a16040182. License CC BY 4.0
Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)
Dokmanić, I., Parhizkar, R., Ranieri, J., Vetterli, M.: Euclidean distance matrices: essential theory, algorithms, and Applications. IEEE Signal Process. Mag. 32(6), 12–30 (2015). https://doi.org/10.1109/MSP.2015.2398954
Ziarani, R.J., Ravanmehr, R.: Serendipity in recommender systems: a systematic literature review. J. Comput. Sci. Technol. 36(2), 375–396 (2021). https://doi.org/10.1007/s11390-020-0135-9
Bellogín, A., Cantador, I., Castells, P.: A study of heterogeneity in recommendations for a social music service. In: Proceedings of the 2010 International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 150–157. IEEE, Toronto (2010). https://doi.org/10.1109/WI-IAT.2010.39
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Domoto, H., Uchiya, T., Takumi, I. (2025). Improving User Satisfaction Through Approaches that Balance Recommendation Accuracy and Serendipity Tailored to Individual Preferences. In: Wu, S., Su, X., Xu, X., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2024. Lecture Notes in Computer Science(), vol 15372. Springer, Singapore. https://doi.org/10.1007/978-981-96-0026-7_6
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