  Recommender system has attracted lots of attentions since it helps users
alleviate the information overload problem. Matrix factorization technique 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, based on the
intuition that additional information provides useful insights for matrix
factorization techniques, several recommendation algorithms have utilized
additional information to improve the performance of matrix factorization
methods. However, the majority 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 attributes coupling based item enhanced matrix factorization method
by incorporating item attribute information into matrix factorization technique
as well as adapting the coupled object similarity to capture the relationship
between items. Item attribute information is formed as an item relationship
regularization term to regularize the process of matrix factorization.
Specifically, the similarity between items is measured by the Coupled Object
Similarity considering coupling between items. Experimental results on two real
data sets show that our proposed method outperforms state-of-the-art
recommendation algorithms and can effectively cope with the cold start item
problem when more item attribute information is available.
