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Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen · Shuang Li · Hui Li · Shaohua Jiang · Yuan Qi · Le Song

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #252

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 Cascading 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.

Author Information

Xinshi Chen (Georgia Institution of Technology)
Shuang Li (Georgia Tech)
Hui Li (Ant Financial)
Shaohua Jiang (Ant Financial)
Yuan Qi (Ant Financial Services Group)
Le Song (Georgia Institute of Technology)

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