Mutual Knowledge Distillation and Contrastive Learning between Multi-View Graphs for Cross-Domain Recommendation

As a powerful tool to alleviate the data sparsity and cold-start problems in traditional recommender systems, cross-domain recommendation hinges on addressing two fundamental questions: how to transfer knowledge and what to transfer. Regarding the two questions, existing methods have limitations, such as restricted domain connections, inadequate representation disentanglement, and insufficient knowledge transfer. To overcome these challenges, we propose a novel model, KDCLM, which integrates sophisticated knowledge distillation and contrastive learning mechanisms within a multi-view graph architecture. The proposed model comprises two views—a local view and a global view—both of which construct multiple graphs based on user-item interactions to establish richer domain connections. Specifically, the local view incorporates two contrastive learning mechanisms: one for aligning domain-invariant representations and another for differentiating domain-specific representations, which jointly achieve effective representation disentanglement. In addition, we employ knowledge distillation between the global heterogeneous user-item interaction graph and the homogeneous user-user and item-item relationship graphs to facilitate sufficient knowledge transfer. Through extensive experiments on real-world cross-domain recommendation tasks, our proposed KDCLM model demonstrates significant improvements over current state-of-the-art methods. We release our source code at https://github.com/fanydan/KDCLM .