mlsanityData to 7 Oct 2026

RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents

arXiv 2308.09904 ↗First posted 19 Aug 2023cs.IR cs.AIcited by 30

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in Human-Computer Interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems' responsibility, and a human-centered approach is vital. We introduce the RAH Recommender system, Assistant, and Human) framework, an innovative solution with LLM-based agents such as Perceive, Learn, Act, Critic, and Reflect, emphasizing the alignment with user personalities. The framework utilizes the Learn-Act-Critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 30 of its 65 references that are in the map. First posted on arXiv 19 Aug 2023.

Builds on

30 of 65 references.

References · 30

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  5. 2017Neural Collaborative Filteringcited by 1,108
  6. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  7. 2023A Survey on Large Language Models for Recommendationcited by 378
  8. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  9. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 246
  10. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  11. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  12. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
18 more
  1. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  2. 2019MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendationcited by 119
  3. 2022A Survey on the Fairness of Recommender Systemscited by 102
  4. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  5. 2017Causal Embeddings for Recommendationcited by 79
  6. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  7. 2021Empowering News Recommendation with Pre-trained Language Modelscited by 63
  8. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  9. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  10. 2022TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twittercited by 44
  11. 2022A Survey on Trustworthy Recommender Systemscited by 40
  12. 2021A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directionscited by 30
  13. 2023Personalized Graph Signal Processing for Collaborative Filteringcited by 29
  14. 2021Tiny-NewsRec: Effective and Efficient PLM-based News Recommendationcited by 16
  15. 2022Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendationcited by 14
  16. 2022Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendationcited by 9
  17. 2022Cross Pairwise Ranking for Unbiased Item Recommendationcited by 8
  18. 2021Practical and Secure Federated Recommendation with Personalized Maskscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 17; 2026: 9.

30 papers.

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  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  3. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 81
  4. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  5. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applicationscited by 40
  6. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  7. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Modelscited by 25
  8. 2025AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendationscited by 23
  9. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systemscited by 23
  10. 2025PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applicationscited by 21
  11. 2025STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarkingcited by 8
  12. 2024A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questionscited by 7
18 more
  1. 2025CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Modelscited by 5
  2. 2026From Atom to Community: Structured and Evolving Agent Memory for User Behavior Modelingcited by 3
  3. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 3
  4. 2025Listwise Preference Alignment Optimization for Tail Item Recommendationcited by 2
  5. 2026AgenticTagger: Structured Item Representation for Recommendation with LLM Agentscited by 2
  6. 2026RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systemscited by 2
  7. 2025Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agentscited by 1
  8. 2025StaffPro: an LLM Agent for Joint Staffing and Profilingcited by 1
  9. 2025"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youthcited by 1
  10. 2025Cloud-Device Collaborative Agents for Sequential Recommendationcited by 1
  11. 2025MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedbackcited by 1
  12. 2025MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender Systemcited by 1
  13. 2026SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seekingcited by 1
  14. 2025ImpReSS: Implicit Recommender System for Support Conversationscited by 0
  15. 2026IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inferencecited by 0
  16. 2026Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaborationcited by 0
  17. 2026PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streamscited by 0
  18. 2026ElderBench: Benchmarking Autonomous Mobile Agents for Older Adultscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation
  2. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
  3. 2023On Generative Agents in Recommendation
  4. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
  5. 2025RecoWorld: Building Simulated Environments for Agentic Recommender Systems
  6. 2025RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
  7. 2024Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems
  8. 2026From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents