User-based collaborative filtering on cross domain by tag transfer learning

Exploiting social tag information has been a popular way to improve recommender systems in recent years. However, recommender systems could not be improved with tags when tags are sparse. We notice that, while the tags are sparse for recommendation on some target domains, related and relatively dense auxiliary tags may already exist in some other more mature application domains. This inspires us to transfer tags to improve recommender systems on cross domain. In this paper, we propose a Tag Transfer Learning (TTL) model for effective cross domain collaborative filtering. TTL has some novel features over traditional collaborative filtering on cross domain. TTL transfers tag topics, a kind of one-way knowledge, instead of user-item rating patterns which is two-way knowledge. TTL is based on the clustering approach but not matrix factorization. TTL also gives a quantitative analysis on "when to transfer". The experiment was conducted on the MovieLens data set. The experimental results reveal that our approach outperforms both the traditional user-based collaborative filtering and the tag-based recommenders.