Incorporating Like-Minded Peers to Overcome Friend Data Sparsity in Session-Based Social Recommendations

TL;DR

This paper introduces LMP to improve social influence modeling in session-based recommendations, effectively addressing friend data sparsity and preference discrepancies, through the novel TEGAARec model with graph attention mechanisms.

Contribution

First to utilize Like-minded Peers for enhancing social influence in SSR, proposing TEGAARec with graph attention modules for better interest modeling.

Findings

TEGAARec outperforms existing models on four datasets.

Incorporating LMP improves recommendation accuracy.

Ablation studies confirm the effectiveness of each component.

Abstract

Session-based Social Recommendation (SSR) leverages social relationships within online networks to enhance the performance of Session-based Recommendation (SR). However, existing SSR algorithms often encounter the challenge of "friend data sparsity". Moreover, significant discrepancies can exist between the purchase preferences of social network friends and those of the target user, reducing the influence of friends relative to the target user's own preferences. To address these challenges, this paper introduces the concept of "Like-minded Peers" (LMP), representing users whose preferences align with the target user's current session based on their historical sessions. This is the first work, to our knowledge, that uses LMP to enhance the modeling of social influence in SSR. This approach not only alleviates the problem of friend data sparsity but also effectively incorporates users with similar preferences to the target user. We propose a novel model named Transformer Encoder with Graph Attention Aggregator Recommendation (TEGAARec), which includes the TEGAA module and the GAT-based social aggregation module. The TEGAA module captures and merges both long-term and short-term interests for target users and LMP users. Concurrently, the GAT-based social aggregation module is designed to aggregate the target users' dynamic interests and social influence in a weighted manner. Extensive experiments on four real-world datasets demonstrate the efficacy and superiority of our proposed model and ablation studies are done to illustrate the contributions of each component in TEGAARec.