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.

翻译：会话式社交推荐通过利用在线网络中的社交关系来提升会话式推荐的性能。然而，现有的SSR算法常常面临"好友数据稀疏性"的挑战。此外，社交网络好友与目标用户的购买偏好之间可能存在显著差异，从而降低了朋友相对于目标用户自身偏好的影响力。为应对这些挑战，本文引入了"志趣相投的同伴"这一概念，指代那些基于其历史会话、偏好与目标用户当前会话相一致的用户。据我们所知，这是首个利用LMP来增强SSR中社交影响力建模的研究。该方法不仅缓解了朋友数据稀疏性问题，还能有效整合与目标用户具有相似偏好的用户。我们提出了一种名为"基于图注意力聚合器的Transformer编码器推荐模型"的新型模型，该模型包含TEGAA模块和基于GAT的社交聚合模块。TEGAA模块负责捕捉并融合目标用户及LMP用户的长期与短期兴趣。同时，基于GAT的社交聚合模块旨在以加权方式聚合目标用户的动态兴趣与社交影响力。在四个真实数据集上的大量实验证明了我们所提模型的有效性与优越性，并通过消融研究阐明了TEGAARec中各组件的贡献。