AI RESEARCH
VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
arXiv CS.LG
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ArXi:2507.21563v4 Announce Type: replace-cross 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.