Reinforcement Learning using Fully Connected Neural Networks in Recommender System

Abstract

Markov decision processes are applied to recommender system in previous research. Reinforcement learning methods using neural networks have been proposed in many fields. But a reinforcement learning method using neural networks has not been proposed in recommender system. In this research we propose a reinforcement learning method using fully connected neural networks in recommender system under the condition that the true parameters of Markov decision processes are unknown. The proposed method uses historical data of customers to represent customers’ properties. The effectiveness of the proposed method is shown by some simulations. The output of the proposed method is equal to the optimal solution in the simulation result.