Cross-domain collaborative filtering via bilinear multilevel analysis

Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity of domains may lead to improper knowledge transfer issues. To address this problem, we propose a novel CDCF model, the Bilinear Multilevel Analysis (BLMA), which seamlessly introduces multilevel analysis theory to the most successful collaborative filtering method, matrix factorization (MF). Specifically, we employ BLMA to more efficiently address the determinants of ratings from a hierarchical view by jointly considering domain, community, and user effects so as to overcome the issues caused by traditional MF approaches. Moreover, a parallel Gibbs sampler is provided to learn these effects. Finally, experiments conducted on a real-world dataset demonstrate the superiority of the BLMA over other state-of-the-art methods.