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Accurate and Diverse Recommendation based on Users' Tendencies toward Temporal Item Popularity

Popularity bias is a phenomenon associated with collaborative filtering algorithms, in which popular items tend to be recommended over unpopular items. As the appropriate level of item popularity differs depending on individual users, a user-level modification approach can produce diverse recommendations while improving the recommendation accuracy. However, there are two issues with conventional user-level approaches. First, these approaches do not isolate users’ preferences from their tendencies toward item popularity clearly. Second, they do not consider temporal item popularity, although item popularity changes dynamically over time in reality. In this paper, we propose a novel approach to counteract the popularity bias, namely, matrix factorization based collaborative filtering incorporating individual users’ tendencies toward item popularity. Our model clearly isolates users’ preferences from their tendencies toward popularity. In addition, we consider the temporal item popularity and incorporate it into our model. Experimental results using a real-world dataset show that our model improve both accuracy and diversity compared with a baseline algorithm in both static and time-varying models. Moreover, our model outperforms conventional approaches in terms of accuracy with the same diversity level. Furthermore, we show that our proposed model recommends items by capturing users’ tendencies toward item popularity: it recommends popular items for the user who likes popular items, while recommending unpopular items for those who don’t like popular items.