Self-Attentive Subset Learning over a Set-Based Preference in Recommendation

Abstract

Recommender systems that learn user preference from item-level feedback (provided to individual items) have been extensively studied. Considering the risk of privacy exposure, learning from set-level feedback (provided to sets of items) has been demonstrated to be a better way, since set-level feedback reveals user preferences while to some extent, hiding his/her privacy. Since only set-level feedback is provided as a supervision signal, different methods are being investigated to build connections between set-based preferences and item-based preferences. However, they overlook the complexity of user behavior in real-world applications. Instead, we observe that users’ set-level preference can be better modeled based on a subset of items in the original set. To this end, we propose to tackle the problem of identifying subsets from sets of items for set-based preference learning. We propose a policy network to explicitly learn a personalized subset selection strategy for users. Given the complex correlation between items in the set-rating process, we introduce a self-attention module to make sure all set members are considered in subset selecting process. Furthermore, we introduce gumble softmax to avoid gradient vanishing caused by binary selection in model learning. Finally the selected items are aggregated by user-specific personalized positional weights. Empirical evaluation with real-world datasets verifies the superiority of the proposed model over the state-of-the-art.