Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

• URL: http://arxiv.org/abs/2104.02981v1
• Date: Wed, 7 Apr 2021 08:13:32 GMT
• Title: Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
• Authors: Kai Wang, Zhene Zou, Qilin Deng, Runze Wu, Jianrong Tao, Changjie Fan, Liang Chen, Peng Cui
• Abstract summary: We develop a model-based reinforcement learning framework with a disentangled universal value function, called GoalRec. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application.
• Score: 35.79993074465577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. All these problems remain largely unexplored in the existing literature and make the application of RL challenging. We develop a model-based reinforcement learning framework with a disentangled universal value function, called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed. It can generalize to various goals that the recommender may have, and disentangle the stochastic environmental dynamics and high-variance reward signals accordingly. As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-irrelevant world model is trained to simulate complex environmental dynamics under a certain goal. Based on the predicted environmental dynamics, the disentangled universal value function is related to the user's future trajectory instead of a monolithic state and a scalar reward. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application.

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