mlsanityData to 7 Oct 2026

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

arXiv 2403.06465 ↗First posted 11 Mar 2024cs.IR cs.AIcited by 20

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Abstract (arXiv)

This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted perspectives. The new generation of recommender systems, empowered by LLMs, are expected to be more versatile, explainable, conversational, and controllable, paving the way for more intelligent and user-centric recommendation experiences. We hope the open-source of RecAI can help accelerate evolution of new advanced recommender systems. The source code of RecAI is available at urlhttps://github.com/microsoft/RecAI.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 11 Mar 2024.

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5 of 6 references.

References · 5

  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  2. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  3. 2024Aligning Large Language Models for Controllable Recommendationscited by 19
  4. 2023Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendationscited by 16
  5. 2024Aligning Language Models for Versatile Text-based Item Retrievalcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 10; 2026: 6.

20 papers.

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  1. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  2. 2024Explaining Length Bias in LLM-Based Preference Evaluationscited by 48
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  4. 2025Memory Assisted LLM for Personalized Recommendation Systemcited by 7
  5. 2025Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified Viewcited by 5
  6. 2026Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoningcited by 4
  7. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
  8. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 3
  9. 2024TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiencycited by 1
  10. 2025Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Studycited by 1
  11. 2025Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approachescited by 1
  12. 2025Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challengescited by 1
8 more
  1. 2026ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Managementcited by 1
  2. 2024Visual Editing with LLM-based Tool Chaining: An Efficient Distillation Approach for Real-Time Applicationscited by 0
  3. 2025A Text-Based Recommender System that Leverages Explicit Affective State Preferencescited by 0
  4. 2025Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworkscited by 0
  5. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  6. 2026ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shoppingcited by 0
  7. 2026Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivitycited by 0
  8. 2026AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scalecited by 0