  Collaborative filtering is the most popular approach for recommender systems.
One way to perform collaborative filtering is matrix factorization, which
characterizes user preferences and item attributes using latent vectors. These
latent vectors are good at capturing global features of users and items but are
not strong in capturing local relationships between users or between items. In
this work, we propose a method to extract the relationships between items and
embed them into the latent vectors of the factorization model. This combines
two worlds: matrix factorization for collaborative filtering and item embed-
ding, a similar concept to word embedding in language processing. Our
experiments on three real-world datasets show that our proposed method
outperforms competing methods on top-n recommendation tasks.
