How "Like-minded Peers" Enhanced Session-Based Social Recommendation

Yunhan Li, Chunyan An, Qiang Yang, Winston K. G. Seah, Conghao Yang

Abstract: Session-based Social Recommendation (SSR) harnesses social relationships within online social networks to improve Session-based Recommendation (SR) performance. However, existing SSR algorithms often face the challenge of "friend data sparsity". Additionally, significant discrepancies may exist between the purchase preferences of social network friends and those of the target user, thereby diminishing the influence of friends relative to the target user’s preferences. To tackle these challenges, this paper introduces the concept of "Like-minded Peers" (LMP), representing users whose preferences are aligned with the target user’s current session in their history sessions. To the best of our knowledge, this is the first work to use LMP as an enhancement for modeling the social influence in SSR. It not only alleviates the problem of friend data sparseness but also effectively incorporates users with similar preferences. Furthermore, we propose a novel model named Transformer Encoder with Graph Attention Aggregator Recommendation. Experimental results on four real-world datasets demonstrates the efficacy and superiority of our proposed model.