Neural Model-Based Reinforcement Learning for Recommendation

Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Le Song

Abstract:

There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging.
In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.

Keywords: Generative adversarial user model, Recommendation system, combinatorial recommendation policy, model-based reinforcement learning, deep Q-networks

TL;DR: A new insight of designing a RL recommendation policy based on a generative adversarial user model.