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

翻译：跨域序列推荐器（CSRs）正获得广泛研究关注，因为它们能利用来自多个领域的辅助信息捕捉用户的序列偏好。然而，这些工作通常遵循理想设定，即不同领域服从相似的数据分布，这忽略了非对称交互密度所带来的偏差（即域间密度偏差）。此外，序列编码器中频繁采用的机制（如自注意力网络）仅关注局部视图内的交互，忽略了不同训练批次之间的全局关联。为此，我们提出一种外部注意力增强的图对比学习框架，即EA-GCL。具体而言，为消除域间密度偏差的影响，我们在多任务学习方式下，将辅助的自监督学习（SSL）任务附加到传统图编码器中。为鲁棒地捕捉用户行为模式，我们开发了一种基于外部注意力的序列编码器，该编码器包含基于MLP的记忆共享结构。与自注意力机制不同，这种结构能有效缓解批次训练方案带来的偏差干扰。在两个真实数据集上的大量实验表明，EA-GCL在跨域序列推荐任务上优于多个最先进的基线方法。源代码及相关数据集可在https://github.com/HoupingY/EA-GCL获取。