Abstract
In recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. Although many methods have analyzed user popularity preferences, most of them only consider particular types of popularity preferences, leading to inappropriate recommendations for users who have other popularity preferences. To comprehensively study user popularity preferences, we propose a User Popularity preference aware Sequential Recommendation (UPSR) method. By sequentially perceiving user behaviors, UPSR captures the type and the evolution of user popularity preferences. Furthermore, UPSR employs contrastive learning to gather similar users and enhance user interest encoding. Then, we can match items and user popularity preferences more accurately and make more proper recommendations. Extensive experiments validate that UPSR not only outperforms the state-of-the-art methods but also reduces popularity bias.
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This work is supported by program XDC02050200.
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Qian, M., Dai, F., Gu, X., Fan, H., Liu, D., Li, B. (2023). User Popularity Preference Aware Sequential Recommendation. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_8
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