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On ranking the effectiveness of searches

Published: 06 August 2006 Publication History

Abstract

There is a growing interest in estimating the effectiveness of search. Two approaches are typically considered: examining the search queries and examining the retrieved document sets. In this paper, we take the latter approach. We use four measures to characterize the retrieved document sets and estimate the quality of search. These measures are (i) the clustering tendency as measured by the Cox-Lewis statistic, (ii) the sensitivity to document perturbation, (iii) the sensitivity to query perturbation and (iv) the local intrinsic dimensionality. We present experimental results for the task of ranking 200 queries according to the search effectiveness over the TREC (discs 4 and 5) dataset. Our ranking of queries is compared with the ranking based on the average precision using the Kendall t statistic. The best individual estimator is the sensitivity to document perturbation and yields Kendall t of 0.521. When combined with the clustering tendency based on the Cox-Lewis statistic and the query perturbation measure, it results in Kendall t of 0.562 which to our knowledge is the highest correlation with the average precision reported to date.

References

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S. Cronen-Townsend, Y. Zhou and B. Croft. Predicting Query Performance. Proceedings of the 25th Annual International ACM SIGIR conference on Research and Development in Information Retrieval. Tampere, Finland, 2002
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G. Amati, C. Carpineto and G. Romano. Query difficulty, robustness and selective application of query expansion. In Proceedings of the 25th European Conference on Information Retrieval. Sunderland, Great Britain, 2004
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B. He and I. Ounis. Inferring Query Performance Using Pre-retrieval Predictors. In Proceedings of the 11th Symposium on String Processing and Information Retrieval, Padova, Italy, 2004
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C. J. van Rijsbergen. Information Retrieval. Butterworths, London, Second Edition, 1979
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A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice-Hall Advanced Reference Series, Year : 1988
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T. F. Cox and T. Lewis. A conditional distance ratio method for analyzing spatial patterns. Biometrika 63, 483--491, 1976
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A. Tombros and C.J. van Rijsbergen. Query-sensitive similarity measures for Information Retrieval. Invited paper, Knowledge and Information Systems, 2004
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K. Fukunaga and D.R. Olsen. An Algorithm for finding intrinsic dimensionality of data. IEEE Transactions on Computers, C-20(2), pp. 176--183, 1971
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Cited By

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  • (2023)Noisy Perturbations for Estimating Query Difficulty in Dense RetrieversProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615270(3722-3727)Online publication date: 21-Oct-2023
  • (2023)Uncertainty-Wise Model Evolution with Genetic Programming2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C60940.2023.00062(843-844)Online publication date: 22-Oct-2023
  • (2022)Analytics Methods to Understand Information Retrieval Effectiveness—A SurveyMathematics10.3390/math1012213510:12(2135)Online publication date: 19-Jun-2022
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    cover image ACM Conferences
    SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
    August 2006
    768 pages
    ISBN:1595933697
    DOI:10.1145/1148170
    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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    Publication History

    Published: 06 August 2006

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    1. query performance prediction

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    SIGIR06: The 29th Annual International SIGIR Conference
    August 6 - 11, 2006
    Washington, Seattle, USA

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    View all
    • (2023)Noisy Perturbations for Estimating Query Difficulty in Dense RetrieversProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615270(3722-3727)Online publication date: 21-Oct-2023
    • (2023)Uncertainty-Wise Model Evolution with Genetic Programming2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C60940.2023.00062(843-844)Online publication date: 22-Oct-2023
    • (2022)Analytics Methods to Understand Information Retrieval Effectiveness—A SurveyMathematics10.3390/math1012213510:12(2135)Online publication date: 19-Jun-2022
    • (2022)Groupwise Query Performance Prediction with BERTAdvances in Information Retrieval10.1007/978-3-030-99739-7_8(64-74)Online publication date: 10-Apr-2022
    • (2020)Query Performance Prediction for Multifield Document RetrievalProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409821(49-52)Online publication date: 14-Sep-2020
    • (2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
    • (2019)Information Needs, Queries, and Query Performance PredictionProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331253(395-404)Online publication date: 18-Jul-2019
    • (2019)Estimating Gaussian mixture models in the local neighbourhood of embedded word vectors for query performance predictionInformation Processing and Management: an International Journal10.1016/j.ipm.2018.10.00956:3(1026-1045)Online publication date: 1-May-2019
    • (2019)Investigation of Passage Based Ranking Models to Improve Document RetrievalKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-15640-4_6(100-117)Online publication date: 15-Mar-2019
    • (2018)An Extended Query Performance Prediction Framework Utilizing Passage-Level InformationProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234946(35-42)Online publication date: 10-Sep-2018
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