Movie Recommendation using Unrated Data

Model based movie recommender systems have been thoroughly investigated in the past few years, and they rely on rating data. In this paper, we take into account unrated data of genre information to improve the performance of movie recommendation. We propose a novel method to measure users' preference on movie genres, and use Pearson Correlation Coefficient (PCC) to compute the user similarity. A matrix factorization framework is introduced for genre preference regularization. Experimental results on MovieLens data set demonstrate that the approach performs well. Our method can also be used to increase the genre diversity of recommendations to some extent.