Modeling Users’ Multifaceted Interest Correlation for Social Recommendation

Abstract

Recommender systems suggest to users the items that are potentially of their interests, by mining users’ feedback data on items. Social relations provide an independent source of information about users and can be exploited for improving recommendation performance. Most of existing recommendation methods exploit social influence by refining social relations into a scalar indicator to either directly recommend friends’ visited items to users or constrain that friends’ embeddings are similar. However, a scalar indicator cannot express the multifaceted interest correlations between users, since each user’s interest is distributed across multiple dimensions. To address this issue, we propose a new embedding-based framework, which exploits users’ multifaceted interest correlation for social recommendation. We design a dimension-wise attention mechanism to learn a correlation vector to characterize the interest correlation between a pair of friends, capturing the high variation of users’ interest correlation on multiple dimensions. Moreover, we use friends’ embeddings to smooth a user’s own embedding with the correlation vector as weights, building the elaborate unstructured social influence between users. Experimental results on two real-world datasets demonstrate that modeling users’ multifaceted interest correlations can significantly improve recommendation performance.