  Recommender systems are emerging technologies that nowadays can be found in
many applications such as Amazon, Netflix, and so on. These systems help users
to find relevant information, recommendations, and their preferred items.
Slightly improvement of the accuracy of these recommenders can highly affect
the quality of recommendations. Matrix Factorization is a popular method in
Recommendation Systems showing promising results in accuracy and complexity. In
this paper we propose an extension of matrix factorization which adds general
neighborhood information on the recommendation model. Users and items are
clustered into different categories to see how these categories share
preferences. We then employ these shared interests of categories in a fusion by
Biased Matrix Factorization to achieve more accurate recommendations. This is a
complement for the current neighborhood aware matrix factorization models which
rely on using direct neighborhood information of users and items. The proposed
model is tested on two well-known recommendation system datasets: Movielens100k
and Netflix. Our experiment shows applying the general latent features of
categories into factorized recommender models improves the accuracy of
recommendations. The current neighborhood-aware models need a great number of
neighbors to acheive good accuracies. To the best of our knowledge, the
proposed model is better than or comparable with the current neighborhood-aware
models when they consider fewer number of neighbors.
