Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering
Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, Wayne Xin Zhao · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. However, existing GNN-based CF models suffer from noisy user-item interaction data, which seriously affects the effectiveness and robustness in real-world applications. Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising.