A CCA-Based Item-Side Alignment Method for Cross-Domain Recommendation System

Abstract

For cross-domain recommendation, it can be divided into strong correlation and weak correlation problems according to the consistency between auxiliary domain and target domain. The weak correlation problem is more practical than the strong correlation problem, and the solution is more difficult. The difficulty lies in how to establish an effective transfer model, to make sure the auxiliary domain and the target domain can perform effective collaborative training. For weak correlation problem, if the item-side of auxiliary domain and the target domain are not aligned, or the transfer model has a strong dependency on the user-side of the auxiliary domain, it will seriously affect the effect of cross-domain recommendation. To solve these problems mentioned above, we propose a CCA-based item-side alignment method (CIAM) by introducing: (1) item side alignment method. We use CCA to align the item side between auxiliary and target domain, to intensify the weak correlation between 2 domains. (2) the transfer model of retaining the user feature of target domain. The CIAM retained user features of target domain in UV decomposition, that makes the transfer model could not destroy the user feature between 2 domains. The proposed CIAM can improve the assistance of auxiliary domain, and can avoid the influence of the needless user-side of the auxiliary do-main on cross-domain recommendation. By experimental analysis, it can be verified that the proposed CIAM algorithm has a better performance than general cross-domain recommendation methods.

Keywords

• CCA
• cross-domain recommendation
• item-side alignment
• transfer learning