mlsanityData to 13 Oct 2026

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

arXiv 1802.06501 ↗First posted 19 Feb 2018cs.IR cs.LG stat.MLcited by 71

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Abstract (arXiv)

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback. Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations. However, the number of negative feedback is much larger than that of positive one; thus incorporating them simultaneously is challenging since positive feedback could be buried by negative one. In this paper, we develop a novel approach to incorporate them into the proposed deep recommender system (DEERS) framework. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of both positive and negative feedback in recommendations.

Key facts. Cited by 71 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Feb 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 15; 2022: 14; 2023: 11; 2024: 10; 2025: 18; 2026: 3.

71 papers.

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  1. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  2. 2025Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learningcited by 68
  3. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  4. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 32
  5. 2021AutoLoss: Automated Loss Function Search in Recommendationscited by 31
  6. 2025A Survey of Personalization: From RAG to Agentcited by 27
  7. 2023Multi-Task Recommendations with Reinforcement Learningcited by 26
  8. 2021User Tampering in Reinforcement Learning Recommender Systemscited by 23
  9. 2025Generative Auto-Bidding with Value-Guided Explorationscited by 23
  10. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 20
  11. 2022Reinforcement learning on graphs: A surveycited by 18
  12. 2023A General Offline Reinforcement Learning Framework for Interactive Recommendationcited by 18
38 more
  1. 2023Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLMcited by 18
  2. 2021Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameterscited by 17
  3. 2024Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learningcited by 17
  4. 2021Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulationcited by 16
  5. 2023Two-Stage Constrained Actor-Critic for Short Video Recommendationcited by 16
  6. 2023Reinforcing User Retention in a Billion Scale Short Video Recommender Systemcited by 15
  7. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  8. 2021Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learningcited by 14
  9. 2023Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learningcited by 11
  10. 2025Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrievalcited by 11
  11. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  12. 2022ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actorcited by 10
  13. 2021Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendationcited by 9
  14. 2022Local Policy Improvement for Recommender Systemscited by 9
  15. 2021Supervised Advantage Actor-Critic for Recommender Systemscited by 8
  16. 2022Offline Reinforcement Learning for Mobile Notificationscited by 8
  17. 2022Reinforced Imitative Graph Learning for Mobile User Profilingcited by 8
  18. 2024EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systemscited by 8
  19. 2021Denoising User-aware Memory Network for Recommendationcited by 7
  20. 2023Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenderscited by 7
  21. 2022Meta Policy Learning for Cold-Start Conversational Recommendationcited by 6
  22. 2021Value Penalized Q-Learning for Recommender Systemscited by 4
  23. 2022Multi-objective Optimization of Notifications Using Offline Reinforcement Learningcited by 4
  24. 2024DNS-Rec: Data-aware Neural Architecture Search for Recommender Systemscited by 4
  25. 2024Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Modelscited by 4
  26. 2025SampleLLM: Optimizing Tabular Data Synthesis in Recommendationscited by 4
  27. 2022Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learningcited by 3
  28. 2022BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotioncited by 3
  29. 2024Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systemscited by 3
  30. 2025MoL for LLMs: Dual-Loss Optimization to Enhance Domain Expertise While Preserving General Capabilitiescited by 3
  31. 2025GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networkscited by 3
  32. 2026Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testingcited by 3
  33. 2022Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedbackcited by 2
  34. 2022Constrained Reinforcement Learning for Short Video Recommendationcited by 2
  35. 2024Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedbackcited by 2
  36. 2024UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entitiescited by 2
  37. 2025MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuningcited by 2
  38. 2025Model Merging for Knowledge Editingcited by 2

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