{"ID":2889083,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21563","arxiv_id":"2507.21563","title":"VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation","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.","short_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 enric...","url_abs":"https://arxiv.org/abs/2507.21563","url_pdf":"https://arxiv.org/pdf/2507.21563v4","authors":"[\"Minh-Anh Nguyen\",\"Bao Nguyen\",\"Ha Lan N. T.\",\"Tuan Anh Hoang\",\"Duc-Trong Le\",\"Dung D. Le\"]","published":"2025-07-29T07:51:56Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}