Caihong Mu

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation

Graph collaborative filtering has made great progress in the recommender systems, but these methods often struggle with the data sparsity issue in real-world recommendation scenarios. To mitigate the effect of data sparsity, graph collaborative filtering incorporates contrastive learning as an auxil…