Dual Graph Denoising Model for Social Recommendation

Anchen Li, Bo Yang

Track: User modeling, personalization and recommendation

Keywords: Recommender Systems

Abstract: Graph-based social recommender systems utilize user-item interaction graphs and user-user social graphs to model user preferences. However, their performance can be limited by redundant and noisy information in these two graphs. Although several recommender studies on data denoising exist, most either rely on heuristic assumptions, which limit their adaptability, or use a single model that combines denoising and recommendation, potentially imposing substantial demands on the model capacity. To address these issues, we propose a dual Graph Denoising Social Recommender (GDSR), which consists of two steps: graph denoising and user preference prediction. \textit{First}, we design a denoising module which exploits a dual denoising model to alleviate noises in the interaction and social graphs by performing multi-step noise removal. We develop three kinds of conditions to guide our dual graph denoising paradigm and propose a cross-domain graph optimization strategy to enhance the structure of denoised graphs. \textit{Second}, we devise a recommender module that employs a dual graph learning structure on denoised graphs to generate recommendations. Moreover, we use additional supervision signals to introduce a graph contrastive learning task, enhancing the recommender module's representation quality and robustness. Experiment results show the effectiveness of our GDSR.

Submission Number: 525