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

arXiv: https://arxiv.org/abs/2403.06465 · HTML: https://mlsanity.com/paper/2403.06465/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [Aligning Large Language Models for Controllable Recommendations](https://mlsanity.com/paper/2403.05063/) (2024)
- [Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations](https://mlsanity.com/paper/2311.10779/) (2023)
- [Aligning Language Models for Versatile Text-based Item Retrieval](https://mlsanity.com/paper/2402.18899/) (2024)

## Built on by (most cited first, 20 of 20)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [Explaining Length Bias in LLM-Based Preference Evaluations](https://mlsanity.com/paper/2407.01085/) (2024)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Memory Assisted LLM for Personalized Recommendation System](https://mlsanity.com/paper/2505.03824/) (2025)
- [Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View](https://mlsanity.com/paper/2505.03336/) (2025)
- [Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning](https://mlsanity.com/paper/2604.16966/) (2026)
- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)
- [TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency](https://mlsanity.com/paper/2409.05347/) (2024)
- [Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study](https://mlsanity.com/paper/2506.13464/) (2025)
- [Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches](https://mlsanity.com/paper/2508.02096/) (2025)
- [Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges](https://mlsanity.com/paper/2510.20641/) (2025)
- [ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management](https://mlsanity.com/paper/2601.13631/) (2026)
- [Visual Editing with LLM-based Tool Chaining: An Efficient Distillation Approach for Real-Time Applications](https://mlsanity.com/paper/2410.02952/) (2024)
- [A Text-Based Recommender System that Leverages Explicit Affective State Preferences](https://mlsanity.com/paper/2505.20190/) (2025)
- [Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks](https://mlsanity.com/paper/2506.03391/) (2025)
- [A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2506.11563/) (2025)
- [ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping](https://mlsanity.com/paper/2606.31693/) (2026)
- [Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity](https://mlsanity.com/paper/2608.03272/) (2026)
- [AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale](https://mlsanity.com/paper/2609.16625/) (2026)

## 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.