Generative Inverse Deep Reinforcement Learning for Online Recommendation

• URL: http://arxiv.org/abs/2011.02248v1
• Date: Wed, 4 Nov 2020 12:12:25 GMT
• Title: Generative Inverse Deep Reinforcement Learning for Online Recommendation
• Authors: Xiaocong Chen and Lina Yao and Aixin Sun and Xianzhi Wang and Xiwei Xu and Liming Zhu
• Abstract summary: We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
• Score: 62.09946317831129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation research. Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process. However, most reward functions are manually designed; they are either unrealistic or imprecise to reflect the high variety, dimensionality, and non-linearity properties of the recommendation problem. That makes it difficult for the agent to learn an optimal policy to generate the most satisfactory recommendations. To address the above issue, we propose a novel generative inverse reinforcement learning approach, namely InvRec, which extracts the reward function from user's behaviors automatically, for online recommendation. We conduct experiments on an online platform, VirtualTB, and compare with several state-of-the-art methods to demonstrate the feasibility and effectiveness of our proposed approach.

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