Multi-facet user preference learning for fine-grained item recommendation

Abstract

Existing recommendation methods mainly learn user preference from historical user-item interaction data, while ignoring the extent of interactions, i.e., diverse user experience and user intention. To be more specific, for new users with little or no experience, they may turn to popular items preferred by majority users. In terms of those medium-level users who already have interacted with some items, they may require and expect items to meet their personal preferences. As pro-active users are likely to leave rich behavior (action) feedback (e.g., view, like) on the items they interacted with, the system will have good chance to better interpret users’ intention, and thus generate more accurate and elaborate recommendations to hit their preferences. In this paper, we propose a generic Multi-facet User Preference Learning (MUPL) framework for fine-grained item recommendation. By considering diverse user experience and intention, MUPL captures user preference in the level of group-, individual- and action-facet. Besides, the importance of user preference in different facets can be automatically learnt by MUPL. Extensive experiments on two real-world datasets (Xing, Sobazaar) demonstrate the superiority of our proposed approach over other state-of-the-art methods.