A Combinatorial Recommendation System Framework Based on Deep Reinforcement Learning

In this paper, we propose a combinatorial recommendation system based on deep reinforcement learning (DRL). Specifically, we focus on the combinatorial product recommendation problem, design a consumer behavior simulator, and utilize deep reinforcement learning to find appropriate product combinations that can improve the sales of the platform. In order to replace real users and conduct massive real-time interactive training with recommendation system, user short- term characteristics are extracted from user-click-history by hierarchical recurrent neural network to train a user simulator. Through ingenious modeling, we transform the NP-hard combinatorial optimization problem into a multi-step sequential decision problem, and construct a framework of combinatorial recommendation system based on DRL. Relevant experiments show that the binary accuracy of the user simulator in predicting users’ consumption behavior reaches more than 80%, and the combinatorial DRL based recommendation system improves the platform sales and provides attractive combinations of products for customers.