  Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging
task that depicts the evolution of behavior patterns for overlapped users by
modeling their interactions from multiple domains. Existing studies on CSR
mainly focus on using composite or in-depth structures that achieve significant
improvement in accuracy but bring a huge burden to the model training.
Moreover, to learn the user-specific sequence representations, existing works
usually adopt the global relevance weighting strategy (e.g., self-attention
mechanism), which has quadratic computational complexity. In this work, we
introduce a lightweight external attention-enhanced GCN-based framework to
solve the above challenges, namely LEA-GCN. Specifically, by only keeping the
neighborhood aggregation component and using the Single-Layer Aggregating
Protocol (SLAP), our lightweight GCN encoder performs more efficiently to
capture the collaborative filtering signals of the items from both domains. To
further alleviate the framework structure and aggregate the user-specific
sequential pattern, we devise a novel dual-channel External Attention (EA)
component, which calculates the correlation among all items via a lightweight
linear structure. Extensive experiments are conducted on two real-world
datasets, demonstrating that LEA-GCN requires a smaller volume and less
training time without affecting the accuracy compared with several
state-of-the-art methods.
