Peng-Fei Shao

LRGCL: LLM-refined graph contrastive learning for review-based recommendation

LRGCL is proposed, an LLM-Refined Graph Contrastive Learning model for personalized recommendation that introduces a semantic-preserving contrastive objective in which key interaction edges are identified and preserved in augmented graph views, while only non-critical edges are dropped.