CPR: Cross-Domain Preference Ranking with User Transformation.

Data sparsity is a well-known challenge in recommender systems. One way to alleviate this problem is to leverage knowledge from relevant domains. In this paper, we focus on an important real-world scenario in which some users overlap two different domains but items of the two domains are distinct. Although several studies leverage side information (e.g., user reviews) for cross-domain recommendation, side information is not always available or easy to obtain in practice. To this end, we propose cross-domain preference ranking (CPR) with a simple yet effective user transformation that leverages only user interactions with items in the source and target domains to transform the user representation. Given the proposed user transformation, CPR not only successfully enhances recommendation performance for users having interactions with target-domain items but also yields superior performance for cold-start users in comparison with state-of-the-art cross-domain recommendation approaches. Extensive experiments conducted on three pairs of cross-domain recommendation datasets demonstrate the effectiveness of the proposed method in comparison with existing cross-domain recommendation approaches. Our codes are available at https://github.com/cnclabs/codes.crossdomain.rec .