Cross-Domain Recommendation Via Tag Matrix Transfer

Data sparseness is one of the most challenging problems in collaborative filtering(CF) based recommendation systems. Exploiting social tag information is becoming a popular way to alleviate the problem and improve the performance. To this end, in recent recommendation methods the relationships between users/items and tags are often taken into consideration, however, the correlations among tags from different item-domains are always ignored. For that, in this paper we propose a novel way to exploit the rating patterns across multiple domains by transferring the tag co-occurrence matrix information, which could be used for revealing common user pattern. With extensive experiments we demonstrate the effectiveness of our approach for the cross-domain information recommendation.