mlsanityData to 14 Oct 2026

Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

arXiv 2004.08068 ↗First posted 17 Apr 2020cs.IR cs.LG stat.MLcited by 18

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Abstract (arXiv)

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.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Apr 2020.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 3; 2024: 2; 2025: 3; 2026: 1.

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  1. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 21
  2. 2023Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learningcited by 11
  3. 2023On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systemscited by 10
  4. 2025Modeling Earth-Scale Human-Like Societies with One Billion Agentscited by 8
  5. 2021Generative Adversarial Reward Learning for Generalized Behavior Tendency Inferencecited by 6
  6. 2024Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systemscited by 3
  7. 2024On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systemscited by 1
  8. 2025Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendationcited by 1
  9. 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
  10. 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
  11. 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
  12. 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
6 more
  1. 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
  2. 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
  3. 2022Using Graph Neural Networks for Program Terminationcited by 0
  4. 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
  5. 2025Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluationcited by 0
  6. 2026The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designerscited by 0