Multi Cross Domain Recommendation Using Item Embedding and Canonical Correlation Analysis

In a multi-service environment it is crucial to be able to leverage user behavior from one or more domains to create personalized recommendations in the other domain. In our paper, we present a robust transfer learning approach that successfully captures user behavior across multiple domains. First, we vectorize users and items in each domain independently. Second, using a handful of common users across domain pairs, we project each domain vector space into a common vector space using canonical correlation analysis (CCA). Next, recommendations can be performed by recommending the items in any domains that are closest to the user’s vector in the common space. We also experimented on what kind of domain combination works well.