Incorporating Like-Minded Peers to Overcome Friend Data Sparsity in Session-Based Social Recommendations
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；College of Medicine, University of Florida（佛罗里达大学医学院）；School of Engineering and Computer Science, Victoria University of Wellington（惠灵顿维多利亚大学工程与计算机科学学院）；School of Computer Science, Fudan University（复旦大学计算机学院）
Chunyan An, Yunhan Li, Qiang Yang, Winston K. G. Seah, Zhixu Li, Conghao Yang
基于会话的社交推荐（Session-based Social Recommendation, SSR）利用在线网络中的社交关系来提升基于会话的推荐（Session-based Recommendation, SR）的性能。然而，现有的SSR算法经常面临“好友数据稀疏”的挑战。此外，社交网络好友的购买偏好与目标用户的购买偏好之间可能存在显著差异，从而相对于目标用户自身的偏好降低了好友的影响力。为应对这些挑战，本文提出了“志同道合的同伴”（Like-minded Peers, LMP）概念，指根据其历史会话，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中社会影响建模的工作。该方法不仅缓解了好友数据稀疏问题，还有效纳入了与目标用户具有相似偏好的用户。我们提出了一种名为带图注意力聚合器推荐的Transformer编码器（Transformer Encoder with Graph Attention Aggregator Recommendation, TEGAARec）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模块捕获并融合目标用户和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和社会影响。在四个真实世界数据集上的大量实验证明了我们所提模型的有效性和优越性，并进行了消融研究以说明TEGAARec中每个组件的贡献。
英文摘要：
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.
The Susceptibility Paradox in Online Social Influence
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；Thomas Lord Department of Computer Science, University of Southern California（南加州大学托马斯·洛德计算机科学系）
Estimating Social Influence from Observational Data
Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Columbia University（哥伦比亚大学）
