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.

本文提出了一种轻量级的外部注意力增强 GCN 框架 (LEA-GCN) 来解决交叉领域序列推荐中遇到的挑战，包括用户特定序列表示的学习以及复合或深层结构的高运算复杂度等问题。该方法通过邻域聚合和双通道外部注意力机制捕捉两个域的商品的协同过滤信号实现其更高的运行效率，实验证明其相较于其他最先进方法，所需体积和训练时间更小而不影响准确性。