Your Social Circle Affects Your Interests: Social Influence Enhanced Session-Based Recommendation

Session-based recommendation aims at predicting the next item given a series of historical items a user interacts with in a session. Many works try to make use of social network to achieve a better recommendation performance. However, existing works treat the weights of user edges as the same and thus neglect the differences of social influences among users in a social network, for each user’s social circle differs widely. In this work, we try to utilize an explicit way to describe the impact of social influence in recommender system. Specially, we build a heterogeneous graph, which is composed of users and items nodes. We argue that the fewer neighbors users have, the more likely users may be influenced by neighbors, and different neighbors may have various influences on users. Hence weights of user edges are computed to characterize different influences of social circles on users in a recommendation simulation. Moreover, based on the number of followers and PageRank score of each user, we introduce various computing methods for weights of user edges from a comprehensive perspective. Extensive experiments performed on three public datasets demonstrate the effectiveness of our proposed approach.