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Classical accuracy-oriented Recommender Systems (RSs) typically face thecold-start problem and the filter-bubble problem when users suffer thefamiliar, repeated, and even predictable recommendations, making them boringand unsatisfied. To address the above issues, serendipity-oriented RSs areproposed to recommend appealing and valuable items significantly deviating fromusers' historical interactions and thus satisfying them by introducingunexplored but relevant candidate items to them. In this paper, we devise anovel 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 toenhance the recommendation performance. Specifically, this model extractsusers' interest and satisfaction preferences, generates virtual but convincibleneighbors' preferences from themselves, and achieves their self-serendipitypreference. Then these preferences are injected into the rating matrix asadditional information for RS models. Note that GS$^2$-RS can not only tacklethe cold-start problem but also provides diverse but relevant recommendationsto relieve the filter-bubble problem. Extensive experiments on benchmarkdatasets illustrate that the proposed GS$^2$-RS model can significantlyoutperform the state-of-the-art baseline approaches in serendipity measureswith a stable accuracy performance.

Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems