Fusion of single-domain contrastive embedding and cross-domain graph collaborative filtering network for recommendation systems

When user-item interaction data are extremely sparse or even missing, establishing accurate user interest models is a critical challenge that our work addresses by proposing a novel framework. Cross-domain recommendation has attracted significant attention as an effective approach to address the issues of data sparsity and cold-start problems. However, existing dual-target cross-domain recommendation methods often overlook the long-tail distribution and popularity bias in the interaction data, leading to subpar recommendations for long-tail items and a tendency to favor popular items excessively. Our framework, named Fusion of Single-Domain Contrastive Embedding and Cross-Domain Graph Collaborative Filtering Network (SCE-CGCF), directly addresses these issues by building a unified cross-domain graph network to capture implicit relations among users-items, users-users, and items-items across different domains. By incorporating cross-domain features and cross-domain interaction information into the cross-domain graph network, we effectively leverage both the similarities and differences between domains. Within the cross-domain graph network, we apply graph convolutional networks to learn user and item embeddings, enhancing the feature propagation and addressing the popularity bias problem by better capturing cross-domain collaborative signals. This allows us to generate rich embeddings that more accurately reflect user interests. Additionally, to mitigate the impact of popularity bias, we introduce a contrastive learning method that employs random noise to generate more balanced representations, thereby creating diverse and contrasting views that enrich the recommendation of long-tail items without compromising accuracy. We conduct extensive experiments on three real-world cross-domain recommendation datasets to evaluate the performance of our proposed method. The results demonstrate the significant improvements achieved by our approach compared to state-of-the-art baselines, particularly in enhancing the novelty and coverage of long-tail items by effectively utilizing inter-domain dissimilarities through our graph convolution approach. Our research emphasizes the importance of considering the long-tail distribution and popularity bias in cross-domain recommendation and contributes to the advancement of effective recommendation systems.