# RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning

arXiv: https://arxiv.org/abs/2503.20430 · HTML: https://mlsanity.com/paper/2503.20430/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)
- Retrieval-augmented generation / [RAG for recommendation](https://mlsanity.com/rag/rag-for-recommendation/)

## Builds on (most cited first, 11 of 11)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [Leveraging LLM Reasoning Enhances Personalized Recommender Systems](https://mlsanity.com/paper/2408.00802/) (2024)
- [Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation](https://mlsanity.com/paper/2401.13870/) (2024)
- [Federated Recommendation via Hybrid Retrieval Augmented Generation](https://mlsanity.com/paper/2403.04256/) (2024)
- [ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning](https://mlsanity.com/paper/2410.23180/) (2024)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)

## Built on by (most cited first, 6 of 6)

- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models](https://mlsanity.com/paper/2601.07252/) (2026)
- [LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2602.13571/) (2026)
- [Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents](https://mlsanity.com/paper/2608.19794/) (2026)
- [RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models](https://mlsanity.com/paper/2603.27008/) (2026)
- [Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation](https://mlsanity.com/paper/2610.02968/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have been integrated into recommender systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods have two shortcomings. textit(i) In the textitretrieval stage, they rely primarily on textual semantics and often fail to incorporate the most relevant items, thus constraining system effectiveness. textit(ii) In the textitgeneration stage, they lack explicit chain-of-thought reasoning, further limiting their potential. In this paper, we propose Representation learning and Reasoning empowered retrieval-Augmented Large Language model Recommendation (RALLRec+). Specifically, for the retrieval stage, we prompt LLMs to generate detailed item descriptions and perform joint representation learning, combining textual and collaborative signals extracted from the LLM and recommendation models, respectively. To account for the time-varying nature of user interests, we propose a simple yet effective reranking method to capture preference dynamics. For the generation phase, we first evaluate reasoning LLMs on recommendation tasks, uncovering valuable insights. Then we introduce knowledge-injected prompting and consistency-based merging approach to integrate reasoning LLMs with general-purpose LLMs, enhancing overall performance. Extensive experiments on three real world datasets validate our method's effectiveness.