Exploiting Social Tags for Cross-Domain Collaborative Filtering (1302.4888v2)

Abstract: One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available for making recommendations. Cross-domain collaborative filtering (CDCF) has been studied as an effective mechanism to alleviate data sparseness of one domain using the knowledge about user preferences from other domains. A key question to be answered in the context of CDCF is what common characteristics can be deployed to link different domains for effective knowledge transfer. In this paper, we assess the usefulness of user-contributed (social) tags in this respect. We do so by means of the Generalized Tag-induced Cross-domain Collaborative Filtering (GTagCDCF) approach that we propose in this paper and that we developed based on the general collective matrix factorization framework. Assessment is done by a series of experiments, using publicly available CF datasets that represent three cross-domain cases, i.e., two two-domain cases and one three-domain case. A comparative analysis on two-domain cases involving GTagCDCF and several state-of-the-art CDCF approaches indicates the increased benefit of using social tags as representatives of explicit links between domains for CDCF as compared to the implicit links deployed by the existing CDCF methods. In addition, we show that users from different domains can already benefit from GTagCDCF if they only share a few common tags. Finally, we use the three-domain case to validate the robustness of GTagCDCF with respect to the scale of datasets and the varying number of domains.