Reinforcement learning based path exploration for sequential explainable recommendation

Yicong LI, Hongxu CHEN, Yile LI, Lin LI, Philip S. YU, Guandong XU

Research output: Contribution to journalArticlespeer-review

4 Citations (Scopus)


Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information from knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although some works boost the performance and explainability of recommendations through modeling the user's temporal sequential behavior, most of them either only focus on modeling the user's sequential interactions within a path or independently and separately of the recommendation mechanism. Moreover, some path-based explainable recommendations use random selection or traditional machine learning methods to decrease the volume of explainable paths, which cannot guarantee high quality of the explainable paths for the recommendation. To deal with the problem, recent path exploration use reinforcement learning to improve diversity and quality. However, unsupervised training leads to low-efficiency path exploration. Therefore, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes supervised reinforcement learning to explore item-item paths between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on a dynamic knowledge graph for the explainable recommendation. Extensive evaluations of TMER-RL on two real-world datasets show state-of-the-art performance compared to recent strong baselines. Copyright © 2023 IEEE.

Original languageEnglish
Pages (from-to)11801-11814
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Early online dateJan 2023
Publication statusPublished - Nov 2023


Li, Y., Chen, H., Li, Y., Li, L., Yu, P. S., & Xu, G. (2023). Reinforcement learning based path exploration for sequential explainable recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(11), 11801-11814.


  • Reinforcement learning
  • Sequential recommendation
  • Meta-path
  • Explanation


Dive into the research topics of 'Reinforcement learning based path exploration for sequential explainable recommendation'. Together they form a unique fingerprint.