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.

翻译：跨领域序列推荐（CSR）是一项新兴且具有挑战性的任务，它通过建模重叠用户在多个领域中的交互行为，描绘其行为模式的演变过程。现有CSR研究主要采用复合或深层结构，虽显著提升准确率，却给模型训练带来巨大负担。此外，为学习用户特定的序列表示，现有工作通常采用全局相关性加权策略（如自注意力机制），其计算复杂度为二次方。本文提出一种轻量级外部注意力增强的图卷积网络框架LEA-GCN，以解决上述挑战。具体而言，通过仅保留邻域聚合组件并采用单层聚合协议（SLAP），我们的轻量级GCN编码器能以更高效率捕捉来自两个领域项目的协同过滤信号。为进一步简化框架结构并聚合用户特定的序列模式，我们设计了一种新颖的双通道外部注意力（EA）组件，通过轻量级线性结构计算所有项目间的相关性。在两种真实数据集上的大量实验表明，与多种最先进方法相比，LEA-GCN在保持准确率的同时，所需参数更少且训练时间更短。