VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.
Large language model enhanced hard sample identification for denoising recommendation
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A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
citing papers explorer
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VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
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A Survey on Generative Recommendation: Data, Model, and Tasks
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.