# RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation

arXiv: https://arxiv.org/abs/2312.16018 · HTML: https://mlsanity.com/paper/2312.16018/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2023.

## Built on by (most cited first, 30 of 36)

- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence](https://mlsanity.com/paper/2503.02398/) (2025)
- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Large Language Models as Recommender Systems: A Study of Popularity Bias](https://mlsanity.com/paper/2406.01285/) (2024)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](https://mlsanity.com/paper/2403.03536/) (2024)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation](https://mlsanity.com/paper/2501.05647/) (2025)
- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [In-context Ranking Preference Optimization](https://mlsanity.com/paper/2504.15477/) (2025)
- [Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery](https://mlsanity.com/paper/2403.06097/) (2024)
- [RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning](https://mlsanity.com/paper/2502.06101/) (2025)
- [One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation](https://mlsanity.com/paper/2604.27599/) (2026)
- [SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents](https://mlsanity.com/paper/2606.28356/) (2026)
- [DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation](https://mlsanity.com/paper/2406.11156/) (2024)
- [KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation](https://mlsanity.com/paper/2507.05863/) (2025)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)
- [X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2504.20859/) (2025)
- [Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders](https://mlsanity.com/paper/2601.05588/) (2026)
- [Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs](https://mlsanity.com/paper/2601.08403/) (2026)
- [High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning](https://mlsanity.com/paper/2602.07333/) (2026)
- [RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation](https://mlsanity.com/paper/2603.00638/) (2026)
- [Efficient LLM-based Advertising via Model Compression and Parallel Verification](https://mlsanity.com/paper/2605.11582/) (2026)
- [Generative Archetype-Grounded Item Representations for Sequential Recommendation](https://mlsanity.com/paper/2606.11023/) (2026)
- [Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking](https://mlsanity.com/paper/2502.06802/) (2025)
- [Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval](https://mlsanity.com/paper/2510.12014/) (2025)
- [Top-k on a Budget: Adaptive Ranking with Weak and Strong Oracles](https://mlsanity.com/paper/2601.20989/) (2026)
- [GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm](https://mlsanity.com/paper/2602.01865/) (2026)
- [Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders](https://mlsanity.com/paper/2604.07825/) (2026)
- [Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking](https://mlsanity.com/paper/2605.27429/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized textitprompts to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results. In this paper, we introduce RecRanker, tailored for instruction tuning LLMs to serve as the Ranker for top-textitk Recommendations. Specifically, we introduce an adaptive sampling module for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed RecRanker in both direct and sequential recommendation scenarios.