Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users

Abstract

Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then trains a cross-domain mapping function to encode such distance information by exploiting both overlapping users as labeled data and all the items as unlabeled data. In addition, SSCDR adopts an effective inference technique that predicts the latent vectors of cold-start users by aggregating their neighborhood information. Our extensive experiments on different CDR scenarios show that SSCDR outperforms the state-of-the-art methods in terms of CDR accuracy, particularly in the realistic settings that a small portion of users overlap between two domains.