All Stories

  1. International Workshop on Multimodal Generative Search and Recommendation (MMGenSR@CIKM 2025)
  2. Personalized Image Generation with Large Multimodal Models
  3. A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
  4. Recommendation Unlearning via Influence Function
  5. Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
  6. GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
  7. Large Language Models for Recommendation: Past, Present, and Future
  8. Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
  9. Large Language Models are Learnable Planners for Long-Term Recommendation
  10. Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
  11. The 2nd Workshop on Recommendation with Generative Models
  12. Large Language Models for Recommendation: Progresses and Future Directions
  13. LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
  14. Large Language Models for Recommendation: Progresses and Future Directions
  15. The 1st Workshop on Recommendation with Generative Models
  16. Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
  17. Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
  18. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
  19. Causal Recommendation: Progresses and Future Directions
  20. Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
  21. Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective
  22. Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
  23. Addressing Confounding Feature Issue for Causal Recommendation
  24. Accepted Tutorials at The Web Conference 2022
  25. Causal Intervention for Leveraging Popularity Bias in Recommendation
  26. How to Retrain Recommender System?