VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation

TL;DR

VoteGCL introduces a data augmentation method using LLMs and majority voting to enhance graph-based recommendation systems, addressing data sparsity and popularity bias.

Contribution

It presents a novel LLM-based data augmentation framework with theoretical guarantees, integrated into graph contrastive learning for improved recommendations.

Findings

Improves recommendation accuracy over strong baselines.

Reduces popularity bias in recommendation results.

Provides theoretical guarantees for synthetic data quality.

Abstract

Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify popularity bias in real-world scenarios. This paper proposes a novel data augmentation framework that leverages Large Language Models (LLMs) and item textual descriptions to enrich interaction data. By few-shot prompting LLMs multiple times to rerank items and aggregating the results via majority voting, we generate high-confidence synthetic user-item interactions, supported by theoretical guarantees based on the concentration of measure. To effectively leverage the augmented data in the context of a graph recommendation system, we integrate it into a graph contrastive learning framework to mitigate distributional shift and alleviate popularity bias. Extensive experiments show that our method improves accuracy and reduces popularity bias, outperforming strong baselines.