Deep reinforcement learning for recommender systems

Services that introduce stores to users on the Internet are increasing in recent years. Each service conducts thorough analyses in order to display stores matching each user's preferences. In the field of recommendation, collaborative filtering performs well when there is sufficient click information from users. Generally, when building a user-item matrix, data sparseness becomes a problem. It is especially difficult to handle new users. When sufficient data cannot be obtained, a multi-armed bandit algorithm is applied. Bandit algorithms advance learning by testing each of a variety of options sufficiently and obtaining rewards (i.e. feedback). It is practically impossible to learn everything when the number of items to be learned periodically increases. The problem of having to collect sufficient data for a new user of a service is the same as the problem that collaborative filtering faces. In order to solve this problem, we propose a recommender system based on deep reinforcement learning. In deep reinforcement learning, a multilayer neural network is used to update the value function.