Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation

• URL: http://arxiv.org/abs/2505.24172v1
• Date: Fri, 30 May 2025 03:32:26 GMT
• Title: Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation
• Authors: Lei Sang, Yu Wang, Yiwen Zhang,
• Abstract summary: We introduce a novel model, named Masked Contrastive Learning (MCL), to enhance recommendation robustness to noise.<n>MCL employs a random masking strategy to augment the graph via meta-paths, reducing node sensitivity to specific neighbors and bolstering embedding robustness.<n> Empirical evaluations on three real-world datasets confirm the superiority of our approach over existing recommendation methods.
• Score: 8.711556540753774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated their superiority in exploiting auxiliary information for recommendation tasks. However, graphs constructed using meta-paths in HGNNs are usually too dense and contain a large number of noise edges. The propagation mechanism of HGNNs propagates even small amounts of noise in a graph to distant neighboring nodes, thereby affecting numerous node embeddings. To address this limitation, we introduce a novel model, named Masked Contrastive Learning (MCL), to enhance recommendation robustness to noise. MCL employs a random masking strategy to augment the graph via meta-paths, reducing node sensitivity to specific neighbors and bolstering embedding robustness. Furthermore, MCL employs contrastive cross-view on a Heterogeneous Information Network (HIN) from two perspectives: one-hop neighbors and meta-path neighbors. This approach acquires embeddings capturing both local and high-order structures simultaneously for recommendation. Empirical evaluations on three real-world datasets confirm the superiority of our approach over existing recommendation methods.

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