Yuanyuan Ge

GCIB: Graph Contrastive Information Bottleneck for Multi-Behavior Recommendation

With the rapid emergence of multi-behavior learning in recommender systems, leveraging auxiliary user behaviors has proven effective for mitigating target-behavior data sparsity. Yet auxiliary behavior graphs frequently contain noisy or irrelevant interactions that do not align with the target task,…