  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.
