VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915946577264640 |
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| author | Nguyen, Minh-Anh Nguyen, Bao T., Ha Lan N. Hoang, Tuan Anh Le, Duc-Trong Le, Dung D. |
| author_facet | Nguyen, Minh-Anh Nguyen, Bao T., Ha Lan N. Hoang, Tuan Anh Le, Duc-Trong Le, Dung D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation Nguyen, Minh-Anh Nguyen, Bao T., Ha Lan N. Hoang, Tuan Anh Le, Duc-Trong Le, Dung D. Information Retrieval Machine Learning 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. |
| title | VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2507.21563 |