Ying Guan

CGCLER: Causality-Enhanced Graph Contrastive Learning for Explainable Recommendation

Explainable recommendation has been conceptualized as a joint ranking task encompassing both items and explanations within contemporary recommender system research. The modeling of user–item–explanation triplets can be effectively facilitated by Graph Neural Networks (GNNs) due to their powerful representation learning...