All Stories

  1. Modular Representation Compression: Adapting LLM Representations for Efficient and Effective Recommendation
  2. Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
  3. Action First: Leveraging Preference-Aware Actions for More Effective Decision-Making in Interactive Recommender Systems
  4. Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
  5. Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
  6. MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
  7. Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
  8. DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation
  9. How Can Recommender Systems Benefit from Large Language Models: A Survey
  10. Utility-oriented Reranking with Counterfactual Context
  11. ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
  12. A Bird's-eye View of Reranking: From List Level to Page Level
  13. Multi-Level Interaction Reranking with User Behavior History
  14. U-rank