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Cross-domain sequential recommenders (CSRs) are gaining considerable researchattention as they can capture user sequential preference by leveraging sideinformation from multiple domains. However, these works typically follow anideal setup, i.e., different domains obey similar data distribution, whichignores the bias brought by asymmetric interaction densities (a.k.a. theinter-domain density bias). Besides, the frequently adopted mechanism (e.g.,the self-attention network) in sequence encoder only focuses on theinteractions within a local view, which overlooks the global correlationsbetween different training batches. To this end, we propose an ExternalAttention-enhanced Graph Contrastive Learning framework, namely EA-GCL.Specifically, to remove the impact of the inter-domain density bias, anauxiliary Self-Supervised Learning (SSL) task is attached to the traditionalgraph encoder under a multi-task learning manner. To robustly capture users'behavioral patterns, we develop an external attention-based sequence encoderthat contains an MLP-based memory-sharing structure. Unlike the self-attentionmechanism, such a structure can effectively alleviate the bias interferencefrom the batch-based training scheme. Extensive experiments on two real-worlddatasets demonstrate that EA-GCL outperforms several state-of-the-art baselineson CSR tasks. The source codes and relevant datasets are available athttps://github.com/HoupingY/EA-GCL.

Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation