Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text.

Collaborative Filtering (CF) is the key technique for recommender systems. CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world applications, a user registers for multiple services across websites. Thus it motivates us to exploit both auxiliary and source information for recommendation in this paper. To achieve this, we propose a Transfer Meeting Hybrid (TMH) model for cross-domain recommendation with unstructured text. The proposed TMH model attentively extracts useful content from unstructured text via a memory network and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.