Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement
Yuhan Wang, Qing Jie Xie, Zhifeng Bao, Mengzi Tang, Lin Li, Yongjian Liu · 2025
Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge across domains.Disentangled representation learning provides an effective solution to model complex user preferences by separating intra-domain features (domainshared and domain-specific features), thereby enhancing robustness and interpretability.However, disentanglement-based CDR methods employing generative modeling or GNNs with contrastive objectives face two key challenges: (i) pre-separation strategies decouple features before extracting collaborative signals, disrupting intra-domain interactions and introducing noise; (ii) unsupervised disentanglement objectives lack explicit task-specific guidance, resulting in limited consistency and suboptimal alignment.To address these challenges, we propose DGCDR, a GNN-enhanced encoder-decoder framework.To handle challenge (i), DGCDR first applies GNN to extract high-order collaborative signals, providing enriched representations as a robust foundation for disentanglement.The encoder then dynamically disentangles features into domain-shared and -specific spaces, preserving collaborative information during the separation process.To handle challenge (ii), the