Preference Aware Recommendation Based on Categorical Information

Abstract

Contextual aware matrix factorization has been widely used in recommender systems by learning latent feature vectors of users and items along with contextual information. While most of them add identical bias for each type of side information to represent systematic tendencies in users' rating behaviors, they are not able to capture the preference unique to users or items. In this paper, we propose a probabilistic generative model which allows the bias to vary among different types of users or items. We first use Gaussian Mixture Components to cluster the users (or items) based on corresponding latent feature vectors respectively. Biases are then distributed on these clusters along with categorical side information. Finally, they are jointed with latent feature vectors of the users and items to affect the generation of observed ratings. Experiments on MovieLens-100K and MovieLens-1M data sets have shown promising results compared with state-of-the-art contextual aware recommendation approaches. We also qualitatively analyze the preferences of users and items and demonstrate differences in preference among both users and items.