Review-Based Hierarchical Attention Model Trained with Random Back-Transfer for Cross-Domain Recommendation

Cross-domain recommendation aims to leverage the rich interaction information in the source domain to predict interactions between cold-start users and items in the target domain. Since reviews contain users' preferences and items' attributes, many review-based cross-domain recommendation methods are proposed. However, existing methods cannot either 1) select important words and reviews from multiple reviews of users/items, or 2) learn a unified representation space for different domains without enough overlapping users. To address these problems, we propose a Hierarchical Attention model trained with Random Back-Transfer for cross-domain recommendation (HARBT). Specifically, the hierarchical attention extracts text information related to a given user or item which leads to an accurate interaction prediction. The random back-transfer works as a data augmentation algorithm to utilize data of users and items which are in the same domain for better matching of representations in different domains. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art methods significantly.