Intent-aware Recommendation via Disentangled Graph Contrastive Learning: Understanding User Intents for Effective Recommendations

The author presents the Intent-aware Recommendation via Disentangled Graph Contrastive Learning (IDCL) to simultaneously learn interpretable intents and behavior distributions. The approach involves disentangling user intents, enhancing intent-wise contrastive learning, and introducing coding rate reduction regularization.