Year: 2025
Type: conference-paper
Abstract: Graph-based social recommender systems have demonstrated great potential in alleviating data sparsity by leveraging high-order user influence embedded in social networks.However, most existing methods rely heavily on the observed social graph, which is often noisy and includes spurious or task-irrelevant connections that can mislead user preference learning.Identifying and removing these noisy relations is crucial but challenging due to the lack of ground-truth annotations.In this paper, we approach the social denoising problem from the perspective of graph invariant learning and propose a novel approach, Social Graph Invariant Learning(SGIL).Specifically, SGIL aims to uncover stable user preferences within the input social graph, thereby enhancing the robustness of
Authors: Yonghui Yang, Le Wu, Yuxin Liao, Zhuangzhuang He, Pengyang Shao, Richang Hong, Meng Wang
Institution: Hefei University of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412377082