Dual-domain Collaborative Denoising for Social Recommendation
CoRR(2024)
摘要
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
(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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要