Matrix Factorization Based Models Considering Item Categories and User Neighbors

Matrix factorization is a popular collaborative filtering method for recommendation techniques with predictive accuracy and good scalability. In this paper, we propose two models on the basis of basic matrix factorization, namely CW-MF, NICW-MF. CW-MF considers user´s preference on item categories and NICW-MF takes into account the impact of user´s neighbors to minimize the preference between user and his neighbors. We conduct empirical experiments on MovieLens dataset, and results show that our two models perform well.