Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement

Cross-Domain Recommendation (CDR) is a promising technique to alleviate data sparsity by transferring knowledge across domains. However, the negative transfer issue in the presence of numerous domains has received limited attention. Most existing methods transfer all information from source domains to the target domain without distinction. This introduces harmful noise and irrelevant features, resulting in suboptimal performance. Although some methods decompose user features into domain-specific and domain-shared components, they fail to consider other causes of negative transfer. Worse still, we argue that simple feature decomposition is insufficient for multi-domain scenarios. To bridge this gap, we propose TrineCDR, the TRIple-level kNowledge transferability Enhanced model for multi-target CDR. Unlike previous methods, TrineCDR captures single domain and targeted cross-domain embeddings to serve multi-domain recommendation. For the latter, we identify three fundamental causes of negative transfer, ranging from micro to macro perspectives, and correspondingly enhance knowledge transferability at three different levels: the feature level, the interaction level, and the domain level. Through these efforts, TrineCDR effectively filters out noise and irrelevant information from source domains, leading to more comprehensive and accurate representations in the target domain. We extensively evaluate the proposed model on real-world datasets, sampled from Amazon and Douban, under both dual-target and multi-target scenarios. The experimental results demonstrate the superiority of TrineCDR over state-of-the-art cross-domain recommendation methods.