# LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations

arXiv: https://arxiv.org/abs/2406.12433 · HTML: https://mlsanity.com/paper/2406.12433/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2024.

## Built on by (most cited first, 30 of 40)

- [Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2503.16734/) (2025)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Literature-Grounded Novelty Assessment of Scientific Ideas](https://mlsanity.com/paper/2506.22026/) (2025)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [GR2: Generative Reasoning Re-ranker](https://mlsanity.com/paper/2602.07774/) (2026)
- [DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent](https://mlsanity.com/paper/2603.01152/) (2026)
- [From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation](https://mlsanity.com/paper/2508.09497/) (2025)
- [ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking](https://mlsanity.com/paper/2509.00520/) (2025)
- [One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation](https://mlsanity.com/paper/2604.27599/) (2026)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms](https://mlsanity.com/paper/2601.20131/) (2026)
- [GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks](https://mlsanity.com/paper/2506.16114/) (2025)
- [A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges](https://mlsanity.com/paper/2507.21117/) (2025)
- [PaperAsk: A Benchmark for Reliability Evaluation of LLMs in Paper Search and Reading](https://mlsanity.com/paper/2510.22242/) (2025)
- [ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems](https://mlsanity.com/paper/2509.21371/) (2025)
- [Tree of Preferences for Diversified Recommendation](https://mlsanity.com/paper/2601.02386/) (2025)
- [Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory](https://mlsanity.com/paper/2605.00702/) (2026)
- [Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG](https://mlsanity.com/paper/2411.15491/) (2024)
- [LLM-Enhanced Reranking for Complementary Product Recommendation](https://mlsanity.com/paper/2507.16237/) (2025)
- [Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data](https://mlsanity.com/paper/2511.04541/) (2025)
- [Token-Controlled Re-ranking for Sequential Recommendation via LLMs](https://mlsanity.com/paper/2511.17913/) (2025)
- [Efficient Sequential Recommendation for Long Term User Interest Via Personalization](https://mlsanity.com/paper/2601.03479/) (2026)
- [An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit](https://mlsanity.com/paper/2601.10321/) (2026)
- [Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models](https://mlsanity.com/paper/2601.13533/) (2026)
- [RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2603.10765/) (2026)
- [Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems](https://mlsanity.com/paper/2603.11399/) (2026)
- [Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM](https://mlsanity.com/paper/2609.04738/) (2026)
- [Rethinking Data: Towards Better Performing Domain-Specific Small Language Models](https://mlsanity.com/paper/2503.01464/) (2025)
- [How Reliable are LLMs for Reasoning on the Re-ranking task?](https://mlsanity.com/paper/2508.18444/) (2025)
- [From Noise to Order: Learning to Rank via Denoising Diffusion](https://mlsanity.com/paper/2602.11453/) (2026)

## Abstract (arXiv)

Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration of additional criteria such as diversity and fairness. Existing reranking approaches often fail to harmonize these diverse criteria effectively at the model level. Moreover, these models frequently encounter challenges with scalability and personalization due to their complexity and the varying significance of different reranking criteria in diverse scenarios. In response, we introduce a comprehensive reranking framework enhanced by LLM, designed to seamlessly integrate various reranking criteria while maintaining scalability and facilitating personalized recommendations. This framework employs a fully connected graph structure, allowing the LLM to simultaneously consider multiple aspects such as accuracy, diversity, and fairness through a coherent Chain-of-Thought (CoT) process. A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs. We validate our approach using three popular public datasets, where our framework demonstrates superior performance over existing state-of-the-art reranking models in balancing multiple criteria.