Unleashing the Power of Large Language Model for Denoising Recommendation

TL;DR

This paper presents LLaRD, a novel framework that leverages large language models and a chain-of-thought approach to improve denoising in recommender systems, significantly enhancing recommendation accuracy by filtering noise.

Contribution

Introducing LLaRD, a framework that uses LLMs and a chain-of-thought technique with the information bottleneck principle to improve denoising in recommender systems, addressing limitations of previous methods.

Findings

LLaRD outperforms existing denoising methods in recommendation accuracy.

LLMs effectively generate useful denoising knowledge from observational data.

The chain-of-thought technique enhances relation knowledge extraction for denoising.

Abstract

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.