Dual-domain Collaborative Denoising for Social Recommendation

TL;DR

This paper introduces DCDSR, a dual-domain denoising model for social recommendation that effectively reduces noise in social and interaction data, improving recommendation accuracy.

Contribution

The paper proposes a novel dual-domain collaborative denoising framework with structure-level and embedding-space modules, including a new contrastive learning strategy, Anchor-InfoNCE.

Findings

DCDSR significantly outperforms state-of-the-art methods on three real-world datasets.

The model effectively denoises social network and interaction data, enhancing recommendation performance.

The proposed contrastive learning strategy improves the denoising capability of the model.

Abstract

Social recommendation leverages social network to complement user-item interaction data for recommendation task, aiming to mitigate the data sparsity issue in recommender systems. However, existing social recommendation methods encounter the following challenge: both social network and interaction data contain substaintial noise, and the propagation of such noise through Graph Neural Networks (GNNs) not only fails to enhance recommendation performance but may also interfere with the model's normal training. Despite the importance of denoising for social network and interaction data, only a limited number of studies have considered the denoising for social network and all of them overlook that for interaction data, hindering the denoising effect and recommendation performance. Based on this, we propose a novel model called Dual-domain Collaborative Denoising for Social Recommendation ($\textbf{DCDSR}$). DCDSR comprises two primary modules: the structure-level collaborative denoising module and the embedding-space collaborative denoising module. In the structure-level collaborative denoising module, information from interaction domain is first employed to guide social network denoising. Subsequently, the denoised social network is used to supervise the denoising for interaction data. The embedding-space collaborative denoising module devotes to resisting the noise cross-domain diffusion problem through contrastive learning with dual-domain embedding collaborative perturbation. Additionally, a novel contrastive learning strategy, named Anchor-InfoNCE, is introduced to better harness the denoising capability of contrastive learning. Evaluating our model on three real-world datasets verifies that DCDSR has a considerable denoising effect, thus outperforms the state-of-the-art social recommendation methods.