  A recommender system aims to recommend items that a user is interested in
among many items. The need for the recommender system has been expanded by the
information explosion. Various approaches have been suggested for providing
meaningful recommendations to users. One of the proposed approaches is to
consider a recommender system as a Markov decision process (MDP) problem and
try to solve it using reinforcement learning (RL). However, existing RL-based
methods have an obvious drawback. To solve an MDP in a recommender system, they
encountered a problem with the large number of discrete actions that bring RL
to a larger class of problems. In this paper, we propose a novel RL-based
recommender system. We formulate a recommender system as a gridworld game by
using a biclustering technique that can reduce the state and action space
significantly. Using biclustering not only reduces space but also improves the
recommendation quality effectively handling the cold-start problem. In
addition, our approach can provide users with some explanation why the system
recommends certain items. Lastly, we examine the proposed algorithm on a
real-world dataset and achieve a better performance than the widely used
recommendation algorithm.
