Reinforcement Learning based Recommendation with Graph Convolutional Q-network
Yu Lei, Hongbin Pei, Hanqi Yan, Wenjie Li
Abstract
Reinforcement learning (RL) has been successfully applied to recommender systems. However, the existing RL-based recommendation methods are limited by their unstructured state/action representations. To address this limitation, we propose a novel way that builds high-quality graph-structured states/actions according to the user-item bipartite graph. More specifically, we develop an end-to-end RL agent, termed Graph Convolutional Q-network (GCQN), which is able to learn effective recommendation policies based on the inputs of the proposed graph-structured representations. We show that GCQN achieves significant performance margins over the existing methods, across different datasets and task settings.
Topics & Concepts
Reinforcement learningComputer scienceGraphBipartite graphRecommender systemArtificial intelligenceMachine learningQ-learningTheoretical computer scienceRecommender Systems and TechniquesAdvanced Graph Neural NetworksTopic Modeling