Attributes coupling based matrix factorization for item recommendation

Recommender systems have attracted lots of attention since they alleviate the information overload problem for users. Matrix factorization is one of the most widely employed collaborative filtering techniques in the research of recommender systems due to its effectiveness and efficiency in dealing with very large user-item rating matrices. Recently, additional information, such as social network and user demographics, have been adopted by several recommendation algorithms to provide useful insights for matrix factorization techniques. However, most of them focus on dealing with the cold start user problem and ignore the cold start item problem. In addition, there are few suitable similarity measures for these content enhanced matrix factorization approaches to compute the similarity between categorical items. In this paper, we propose an attributes coupling based matrix factorization method by incorporating item-attribute information into the matrix factorization model as well as adopting coupled object similarity to capture the relationship among items. Item-attribute information is formed as an item relationship regularization term to constrain the process of matrix factorization. Experimental results on two real data sets show that our proposed method outperforms the state-of-the-art recommendation algorithms and can effectively cope with the cold start item problem when such item-attribute information is available.