  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.
