Beyond Users: Denoising Behavior-based Contrastive Learning for Disentangled Cross-Domain Recommendation

Lele Sun, Jing Liu, Shenyuan Zhang, Weizhi Nie, Anan Liu, Yuting Su

Abstract: Cross-Domain Recommendation (CDR) has emerged as an effective solution to the data sparsity issue in Recommender Systems (RS). Existing CDR methods typically disentangle user features into domain-invariant and domain-specific features to avoid negative transfer, known as DCDR. Nevertheless, these methods often neglect the side effects of noisy behaviors (interactions) during disentanglement. Furthermore, they fail to account for item features during disentanglement, which significantly influence the generation of user features. These two critical oversights lead to the degraded performance of existing DCDR methods. To overcome these issues, we introduce a Denoising Contrastive learning framework specifically tailored for DCDR (DCDC). DCDC conducts denoising at both the structure and feature levels. Structure denoising prunes unreliable behaviors, restricting message propagation during graph convolution to reliable edges. Feature denoising modifies the similarity between nodes based on the edge’s reliability. Additionally, we design two contrastive learning (CL) constraints based on user and item mutual information for thorough disentanglement. Contrastive exclusion constraint distinguishes domain-invariant and domain-specific features within a domain, while contrastive proximity constraint minimizes the distance of cross-domain invariant features. The final results showcase the consistent outperformance of our model compared to state-of-the-art methods across three diverse real-world datasets.

External IDs:dblp:conf/dasfaa/SunLZNLS24