Deep-QPP
Author:
Affiliation:
1. University College Dublin, Dublin, Ireland
2. University of Glasgow, Glasgow, United Kingdom
3. Indian Statistical Institute, Kolkata, India
Funder
Science Foundation Ireland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3488560.3498491
Reference47 articles.
1. Pseudo test collections for learning web search ranking functions
2. Query-Performance Prediction Using Minimal Relevance Feedback
3. Adam Byerly , Tatiana Kalganova , and Ian Dear . 2020. A Branching and Merging Convolutional Network with Homogeneous Filter Capsules. CoRR , Vol. abs/ 2001 .09136 ( 2020 ). Adam Byerly, Tatiana Kalganova, and Ian Dear. 2020. A Branching and Merging Convolutional Network with Homogeneous Filter Capsules. CoRR , Vol. abs/2001.09136 (2020).
4. What makes a query difficult?
5. Query Performance Prediction Focused on Summarized Letor Features
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1. Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
2. Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities;Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval;2023-08-09
3. Query-specific Variable Depth Pooling via Query Performance Prediction;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18
4. Query Performance Prediction: From Ad-hoc to Conversational Search;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18
5. iQPP: A Benchmark for Image Query Performance Prediction;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18
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