All Stories

  1. Introduction to the Special Issue on Query Performance Prediction
  2. A Cost-Effective Framework to Evaluate LLM-Generated Relevance Judgements
  3. DP-COMET: A Differential Privacy Contextual Obfuscation MEchanism for Texts in Natural Language Processing
  4. Getting off the DIME: Dimension Pruning via Dimension Importance Estimation for Dense Information Retrieval
  5. Projection-Displacement-Based Query Performance Prediction for Embedded Space of Dense Retrievers
  6. A Comparative Study of Large Language Models and Traditional Privacy Measures to Evaluate Query Obfuscation Approaches
  7. Evaluating Multi-Dimensional Cumulated Utility in Information Retrieval
  8. LLM4Eval: Large Language Model for Evaluation in IR
  9. CoSRec: A Joint Conversational Search and Recommendation Dataset
  10. Variations in Relevance Judgments and the Shelf Life of Test Collections
  11. CoDIME: A Counterfactual Approach for Dimension Importance Estimation through Click Logs
  12. Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024
  13. pyPANTERA: A Python PAckage for Natural language obfuscaTion Enforcing pRivacy & Anonymization
  14. LLM4Eval: Large Language Model for Evaluation in IR
  15. Dimension Importance Estimation for Dense Information Retrieval
  16. Report on the Collab-a-Thon at ECIR 2024
  17. Report on the 13th Italian Information Retrieval Workshop (IIR 2023)
  18. Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities
  19. Perspectives on Large Language Models for Relevance Judgment
  20. A Geometric Framework for Query Performance Prediction in Conversational Search
  21. Modelling and Explaining IR System Performance Towards Predictive Evaluation
  22. Report on the 1st Workshop on Query Performance Prediction and Its Evaluation in New Tasks (QPP++ 2023) at ECIR 2023