Fine-Grained Cross-Domain Recommendation via Two-Tier Attention and Three-Channel Learning

Abstract

Cross-Domain Recommendation (CDR) algorithms, aimed at alleviating the long-standing data sparsity and cold-start problems by transferring information collected from the source domains to the target domains, have attracted increasing attention recently. Other than ratings, existing works on CDR mostly consider side information like tags, reviews, contents etc., yet cannot make full use of texts (i.e. reviews and contents etc.) efficiently or fuse these side information with ratings deeply. Inspired by the advantages shown in review-based recommendations and aspect-based ones, we propose to model fine-grained user preference transfer at aspect level. To achieve this goal, we propose an end2end CDR framework via aspect transfer network with two-tier attention and three-channel learning (named TATCL). TATCL is devised to extract aspects to represent each user or item from their reviews by a review encoder and a subsequent user/item encoder with two-tier attention, and learn accurate aspect correlations across domains with three-channel learning. In addition, we enhance the user and item representation with auxiliary reviews and item contents. Experimental results on datasets demonstrate that, under certain condition, the proposed TATCL has superior predictive performance than existing models in terms of rating prediction accuracy.