Canzhi Chen

LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation

A core barrier preventing recommender systems from reaching their full potential lies in the inherent limitations of user-item interaction data: (1) Sparse user-item interactions, making it difficult to learn reliable user preferences; (2) Traditional contrastive learning methods often treat negativ