Dynamic probabilistic matrix factorization with grey forecast

The goal of recommender system is to find out the items which meet the users' preferences. However users' preferences and items' features change over time that can affect the accuracy of recommender system. Many recommender systems simply employ probabilistic matrix factorization (PMF) model without addressing this issue. Motivated by the grey system theory, in this paper, the dynamics of both users and items are modeled by utilizing the grey forecast (GF) model. Accordingly, a new dynamic recommender system based on probabilistic matrix factorization and grey forecast model (DPMF-GF) is developed. Firstly, the probabilistic matrix factorization (PMF) model is used to produce user's and item's latent vectors between consecutive time windows. Next, the grey forecast model is used to predict user's and item's latent vectors in the following timestamp. The experimental results show that our model can effectively model users' dynamics and items' dynamics, and outperforms the existing state-of-the-art recommendation algorithms. © 2017, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.