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Learning To Rank Resources

Published: 07 August 2017 Publication History

Abstract

We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for selective search, and is statistically equivalent to exhaustive search even for recall-oriented metrics such as MAP@1000, an area in which selective search was lacking.

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Cited By

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  • (2024)FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657853(763-773)Online publication date: 10-Jul-2024
  • (2024)Weighted AUReC: Handling Skew in Shard Map Quality Estimation for Selective SearchAdvances in Information Retrieval10.1007/978-3-031-56066-8_10(87-96)Online publication date: 24-Mar-2024
  • (2023)Learning To Rank Resources with GNNProceedings of the ACM Web Conference 202310.1145/3543507.3583360(3247-3256)Online publication date: 30-Apr-2023
  • Show More Cited By

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cover image ACM Conferences
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2017
1476 pages
ISBN:9781450350228
DOI:10.1145/3077136
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 07 August 2017

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Author Tags

  1. federated search
  2. resource selection
  3. selective search

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657853(763-773)Online publication date: 10-Jul-2024
  • (2024)Weighted AUReC: Handling Skew in Shard Map Quality Estimation for Selective SearchAdvances in Information Retrieval10.1007/978-3-031-56066-8_10(87-96)Online publication date: 24-Mar-2024
  • (2023)Learning To Rank Resources with GNNProceedings of the ACM Web Conference 202310.1145/3543507.3583360(3247-3256)Online publication date: 30-Apr-2023
  • (2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
  • (2019)Report on the DATAACM SIGIR Forum10.1145/3308774.330879452:2(117-124)Online publication date: 17-Jan-2019
  • (2019)LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information RetrievalInformation Retrieval10.1007/978-3-030-31624-2_5(52-63)Online publication date: 18-Sep-2019
  • (2019)Exploiting Global Impact Ordering for Higher Throughput in Selective SearchAdvances in Information Retrieval10.1007/978-3-030-15719-7_2(12-19)Online publication date: 7-Apr-2019
  • (2018)Measuring the Effectiveness of Selective Search Index Partitions without SupervisionProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234952(91-98)Online publication date: 10-Sep-2018
  • (2018)DATAThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210195(1419-1422)Online publication date: 27-Jun-2018
  • (2018)Dynamic Shard Cutoff Prediction for Selective SearchThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210005(85-94)Online publication date: 27-Jun-2018
  • Show More Cited By

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