Semi-supervised Learning for a New Customer Problem in Recommender System

Abstract

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