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）因能利用多领域的辅助信息捕捉用户序列偏好而获得大量研究关注。然而，现有工作通常遵循理想化设定，即不同领域服从相似的数据分布，这忽略了由非对称交互密度（即域间密度偏差）带来的偏差。此外，序列编码器中常用的机制（如自注意力网络）仅关注局部视图内的交互，忽视了不同训练批次间的全局关联。为此，我们提出一种基于外部注意力增强的图对比学习框架EA-GCL。具体而言，为消除域间密度偏差的影响，我们在多任务学习框架下为传统图编码器附加了一项辅助自监督学习任务。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，其包含基于MLP的共享记忆结构。与自注意力机制不同，该结构能有效缓解批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CSR任务上优于多个最先进基线模型。源代码及相关数据集已开源至https://github.com/HoupingY/EA-GCL。