Heterogeneous Debiasing Contrastive Learning for Graph Recommendation

Abstract

Heterogeneous graph neural networks (HGNNs) have demonstrated exceptional capabilities in modeling complex relationships for recommendation tasks. Their integration with contrastive learning (CL) has recently garnered significant attention due to its ability to effectively capture both structural and semantic features, while leveraging unlabeled data to improve robustness. However, existing methods face two key challenges: 1) noise in metapath-based message passing weakens user and item representations, especially with sparse data and 2) popularity bias in heterogeneous graphs causes popular items to dominate, while less popular “tail” items suffer from limited connections and relations. To address these challenges, we propose heterogeneous debiasing CL (HDCL), which combines heterogeneous graph debiasing with a neighborhood-aggregated strategy to enhance recommendation accuracy and diversity. Specifically, HDCL employs a neighborhood-aggregated CL (NACL) algorithm, which utilizes k-nearest neighbor features to enhance training signals and mitigate noise in sparse data settings. Additionally, HDCL introduces a hierarchical clustering-based debiasing (HCD) mechanism that dynamically distinguishes head nodes from tail nodes, refining the embedding space and improving recommendations for long-tail items. Extensive experiments on public datasets demonstrate that HDCL consistently outperforms state-of-the-art methods in terms of Recall and NDCG. The source code for the model implementation is available at the link https://github.com/Jhcodeno1/HDCL