Graph Convolutional Network Based Feature Constraints Learning for Cross-Domain Adaptive Recommendation

The problem of data sparsity is a key challenge for recommendation systems. It motivates the research of cross-domain recommendation (CDR), which aims to use more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, finding useful features to transfer is a challenge for CDR. Avoiding negative transfer while achieving domain adaptation further adds to this challenge. Based on the superiority of graph structural feature learning, this paper proposes a graph convolutional network based Cross-Domain Adaptive Recommendation model using Feature Constraints Learning (CDAR-FCL). To begin with, we construct a multi-graph network consisting of single-domain graphs and one cross-domain graph based on overlapping users. Next, we employ specific and common graph convolution on the graphs to learn domain-specific and domain-invariant features, respectively. Additionally, we design feature constraints on the features obtained in different graphs and mine the potential correlation for domain adaptation. To address the issue of shared parameter conflicts within the constraints, we develop a binary mask learning approach based on contrastive learning. Experiments on three pairs of real cross-domain datasets demonstrate the effectiveness.