Semi-supervised Learning on Recommender System with Transitions of User Classes

Abstract

Markov decision processes(MDP) are applied to recommender system with transitions of user classes. In previous research the true parameters of MDP are known. In this research we propose a semi-supervised learning method for recommender system with transitions of user classes under the condition that the true parameters are unknown. Learning data consist of complete data and incomplete data. In the proposed method EM algorithm is used. The effectiveness of the proposed method is shown by some simulations.