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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 02:25 PM -- 02:30 PM (PDT) @ Room 201

We proposed a novel model-based reinforcement learning framework for recommendation systems, where we developed a GAN formulation to model user behavior dynamics and her associated reward function. Using this user model as the simulation environment, we develop a novel cascading Q-network for combinatorial recommendation policy which can handle a large number of candidate items efficiently. Although the experiments show clear benefits of our method in an offline and realistic simulation setting, even stronger results could be obtained via future online A/B testing.

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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