A Cross-Domain Recommendation Model for Cyber-Physical Systems

Cyber-physical systems (CPS) are often characterized as smart systems, which intelligently interact with other systems across information and physical interfaces. An increased dependence on CPS led to the collection of a vast amount of human-centric data, which brings the information overload problem across multiple domains. Recommender systems in CPS, which always provide information recommendations for users based on historical ratings collected from a single domain only, suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge across multiple domains to alleviate the sparsity problem, which typically assumes that multiple domains share a latent common rating pattern. However, real-world related domains do not necessarily share such a rating pattern, and diversity across domains might outweigh the advantages of such common pattern, which results in performance degradations. In this paper, we propose a novel cross-domain recommendation model, which not only learn the common rating pattern across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns in each domain involving discriminative information propitious to performance improvement. Extensive experiments on real world data sets suggest that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task in CPS.