With the emergence of social networks, social recommendation has become an essential technique for personalized services. Recently, graph-based social recommendations have shown promising results by capturing the high-order social influence. Most empirical studies of graph-based social recommendations directly take the observed social networks into formulation, and produce user preferences based on social homogeneity. Despite the effectiveness, we argue that social networks in the real-world are inevitably noisy~(existing redundant social relations), which may obstruct precise user preference characterization. Nevertheless, identifying and removing redundant social relations is challenging due to a lack of labels. In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective. Specifically, we propose a novel Graph Bottlenecked Social Recommendation (GBSR) framework to tackle the social noise issue.GBSR is a model-agnostic social denoising framework, that aims to maximize the mutual information between the denoised social graph and recommendation labels, meanwhile minimizing it between the denoised social graph and the original one. This enables GBSR to learn the minimal yet sufficient social structure, effectively reducing redundant social relations and enhancing social recommendations. Technically, GBSR consists of two elaborate components, preference-guided social graph refinement, and HSIC-based bottleneck learning. Extensive experimental results demonstrate the superiority of the proposed GBSR, including high performances and good generality combined with various backbones. Our code is available at: https://github.com/yimutianyang/KDD24-GBSR.

翻译：随着社交网络的出现，社交推荐已成为个性化服务的关键技术。近年来，基于图的社交推荐通过捕捉高阶社交影响力展现出优异性能。现有基于图的社交推荐实证研究大多直接利用观测到的社交网络进行建模，并基于社交同质性生成用户偏好。尽管这些方法有效，我们认为现实世界的社交网络不可避免地存在噪声（即冗余社交关系），这可能阻碍精确的用户偏好刻画。然而，由于缺乏标注信息，识别并去除冗余社交关系具有挑战性。本文从信息瓶颈视角出发，研究如何学习去噪的社交结构以提升推荐任务性能。具体而言，我们提出一种新颖的基于图瓶颈的社交推荐框架来解决社交噪声问题。该框架是与模型无关的社交去噪架构，其目标在于最大化去噪社交图与推荐标签之间的互信息，同时最小化去噪社交图与原始社交图之间的互信息。这使得框架能够学习到最简且充分的社交结构，有效减少冗余社交关系并增强社交推荐效果。技术上，该框架包含两个核心组件：偏好引导的社交图优化和基于希尔伯特-施密特独立性准则的瓶颈学习模块。大量实验结果表明，所提框架在结合不同骨干模型时均展现出优越性能，包括更高的推荐精度和良好的泛化能力。代码已开源：https://github.com/yimutianyang/KDD24-GBSR。