SGLCMR: Self-supervised Graph Learning of Generalized Representations for Cross-Market Recommendation

Cross-Market Recommendation (CMR) has been proposed to improve item recommendation performance in the data-insufficient markets by leveraging knowledge learned from other data-sufficient markets. CMR can be regarded as a subtask of Cross-Domain Recommendation (CDR), which overlaps in items and differs in users. Usually, there are a lot of overlapped items in different markets that make the knowledge transfer across markets feasible and valuable. Most existing graph-based methods construct a cross-domain graph by collecting interactions from different domains and then devise some kind of graph neural network (GNN) to learn graph representations for each node. However, we think these methods suffer from two limitations: (1) graph representations are vulnerable to noisy interactions, especially interactions collected from different domains that make the problem more serious; (2) graph representations are biased because they have been more influenced by data-sufficient domains, making graph representations less generalized. In this paper, we propose a novel framework, Self-supervised Graph Learning of Generalized Representations for Cross-Market Recommendation (SGLCMR), to address the foregoing limitations, and we treat different markets as different domains. First, we construct the single-market and cross-market graph to model market-specific and market-generalized features respectively. Second, we adopt Light Graph Convolution (LGC) to aggregate neighbors without loss of generality. Then, we devise two graph-structured data augmentation operators for self-supervised graph learning, i.e., Market-unaware Dropout and Market-aware Dropout, aiming to solve the limitations that graph representations are vulnerable and biased. Finally, we employ a multi-task learning strategy to optimize our model. Extensive experiments on seven pairs of real-world datasets show that our proposed SGLCMR is highly superior to the state-of-the-art CMR methods.