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- W4225105543 abstract "Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied. To address the above issues, serendipity-oriented RSs are proposed to recommend appealing and valuable items significantly deviating from users' historical interactions and thus satisfying them by introducing unexplored but relevant candidate items to them. In this paper, we devise a novel serendipity-oriented recommender system (textbf{G}enerative textbf{S}elf-textbf{S}erendipity textbf{R}ecommender textbf{S}ystem, textbf{GS$^2$-RS}) that generates users' self-serendipity preferences to enhance the recommendation performance. Specifically, this model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference. Then these preferences are injected into the rating matrix as additional information for RS models. Note that GS$^2$-RS can not only tackle the cold-start problem but also provides diverse but relevant recommendations to relieve the filter-bubble problem. Extensive experiments on benchmark datasets illustrate that the proposed GS$^2$-RS model can significantly outperform the state-of-the-art baseline approaches in serendipity measures with a stable accuracy performance." @default.
- W4225105543 created "2022-04-30" @default.
- W4225105543 creator A5012558857 @default.
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- W4225105543 creator A5071710737 @default.
- W4225105543 date "2022-04-26" @default.
- W4225105543 modified "2023-09-26" @default.
- W4225105543 title "Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems" @default.
- W4225105543 doi "https://doi.org/10.48550/arxiv.2204.12651" @default.
- W4225105543 hasPublicationYear "2022" @default.
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