Cross-Domain Recommendation with Multiple Sources

Abstract

Data sparsity remains a challenging and common problem in real-world recommender systems, which impairs the accuracy of recommendation thus damages user experience. Cross-domain recommender systems are developed to deal with data sparsity problem through transferring knowledge from a source domain with relatively abundant data to the target domain with insufficient data. However, two challenging issues exist in cross-domain recommender systems: 1) domain shift which makes the knowledge from source domain inconsistent with that in the target domain; 2) knowledge extracted from only one source domain is insufficient, while knowledge is potentially available in many other source domains. To handle the above issues, we develop a cross-domain recommendation method in this paper to extract group-level knowledge from multiple source domains to improve recommendation in a sparse target domain. Domain adaptation techniques are applied to eliminate the domain shift and align user and item groups to maintain knowledge consistency during the transfer learning process. Knowledge is extracted not from one but multiple source domains through an intermediate subspace and adapted through flexible constraints of matrix factorization in the target domain. Experiments conducted on five datasets in three categories show that the proposed method outperforms six benchmarks and increases the accuracy of recommendations in the target domain.