  The essence of the challenges cold start and sparsity in Recommender Systems
(RS) is that the extant techniques, such as Collaborative Filtering (CF) and
Matrix Factorization (MF), mainly rely on the user-item rating matrix, which
sometimes is not informative enough for predicting recommendations. To solve
these challenges, the objective item attributes are incorporated as
complementary information. However, most of the existing methods for inferring
the relationships between items assume that the attributes are "independently
and identically distributed (iid)", which does not always hold in reality. In
fact, the attributes are more or less coupled with each other by some implicit
relationships. Therefore, in this pa-per we propose an attribute-based coupled
similarity measure to capture the implicit relationships between items. We then
integrate the implicit item coupling into MF to form the Coupled Item-based
Matrix Factorization (CIMF) model. Experimental results on two open data sets
demonstrate that CIMF outperforms the benchmark methods.
