# Unleashing the Power of Large Language Model for Denoising Recommendation

arXiv: https://arxiv.org/abs/2502.09058 · HTML: https://mlsanity.com/paper/2502.09058/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation](https://mlsanity.com/paper/2511.15141/) (2025)
- [RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors](https://mlsanity.com/paper/2601.08705/) (2026)
- [From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals](https://mlsanity.com/paper/2508.10851/) (2025)
- [Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning](https://mlsanity.com/paper/2603.12290/) (2026)
- [AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation](https://mlsanity.com/paper/2605.10367/) (2026)
- [ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation](https://mlsanity.com/paper/2606.05621/) (2026)
- [Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation](https://mlsanity.com/paper/2609.15094/) (2026)

## Abstract (arXiv)

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD's effectiveness in enhancing denoising and recommendation accuracy.