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:交叉序列建议(CSR)是一项新出现的、但具有挑战性的任务,它描述了重叠用户行为模式的变化,通过建模从多个领域进行互动,说明重叠用户的行为模式的变化。关于CSR的现有研究主要侧重于使用综合或深入结构,这些结构在准确性方面有显著提高,但给模型培训带来巨大负担。此外,为了了解用户具体的序列表示,现有工作通常采用具有四进式计算复杂性的全球相关性加权战略(如自留机制),在这项工作中,我们引入了一种轻量的外部关注增强的GCN基础框架,以解决上述挑战,即LEA-GCN。具体地说,仅保留邻里聚合部分,使用单行聚合协议(SLAP),我们的轻量的GCN编码器更有效地捕捉到两个领域项目的协作过滤信号。为了进一步缓解框架结构,汇总用户特有的顺序模式,我们设计了一个新型的双通道外部注意(EA)组件,通过一个轻量线性线性结构计算所有项目之间的关联性。具体地说,仅通过保持邻组群集组件,而使用单行聚合聚合程序,使用单行聚合聚合聚合的聚合程序,在两个空间的实验需要较小的实验室进行两种比较小的数据。