Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

Xiaocong Chen, Chaoran Huang, Lina Yao, Xianzhi Wang, Wei Liu, Wenjie Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)


Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
Publication statusPublished - Jul 2020
Externally publishedYes

Publication series

NameProceedings of the International Joint Conference on Neural Networks


  • Deep Neural Network
  • Recommender System
  • Reinforcement Learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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