All Stories

  1. Investigating User-Side Fairness in Outcome and Process for Multi-Type Sensitive Attributes in Recommendations
  2. Causality-Inspired Fair Representation Learning for Multimodal Recommendation
  3. Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions