DAPEW: Towards robust collaborative filtering with graph contrastive learning

Graph Contrastive Learning (GCL) has shown excellent performance in Collaborative Filtering (CF), one of the most widely used techniques in efficient recommender systems . However, existing GCL-based CF methods suffer from node degree disparity, feature oversmoothing, difficulty in distinguishing hard negative samples, and semantic loss. To address these problems, this paper proposes a novel graph contrastive learning method for robust CF, named D egree- A ware P ropagation and E ntropy- W eighted contrastive loss (DAPEW). DAPEW introduces a degree-aware propagation mechanism to dynamically adjust the influence of initial embeddings, adjacency matrix products, and degree matrix products on the final embeddings, which can effectively handle node degree disparity and alleviate feature oversmoothing. DAPEW also designs an entropy-weighted contrastive loss, which introduces entropy weights to better distinguish hard negative samples and enhance the model’s discriminative ability and robustness. Experimental results show that DAPEW outperforms the existing GCL-based CF methods on several real-world datasets. Compared with existing GCL-based methods, DAPEW improves Recall@40 and NDCG@40 by 0.24% ∼ 25.88% and 0.14% ∼ 26.18% across four different datasets, respectively. • A robust collaborative filtering framework is proposed for recommender systems. • Alleviating node degree disparity can improve recommendation performance. • Entropy-weighted contrastive loss enhances model robustness.