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Using temporal profiles of queries for precision prediction

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SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 18 - 24

https://doi.org/10.1145/1008992.1008998

Published: 25 July 2004 Publication History

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Abstract

A key missing component in information retrieval systems is self-diagnostic tests to establish whether the system can provide reasonable results for a given query on a document collection. If we can measure properties of a retrieved set of documents which allow us to predict average precision, we can automate the decision of whether to elicit relevance feedback, or modify the retrieval system in other ways. We use meta-data attached to documents in the form of time stamps to measure the distribution of documents retrieved in response to a query, over the time domain, to create a temporal profile for a query. We define some useful features over this temporal profile. We find that using these temporal features, together with the content of the documents retrieved, we can improve the prediction of average precision for a query.

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Cited By

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Vuong TJacucci GRuotsalo T(2024)Naturalistic Digital Behavior Predicts Cognitive AbilitiesACM Transactions on Computer-Human Interaction10.1145/366034131:3(1-32)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3660341
Gandhi SMansouri BCampos RJatowt A(2020)Event-Related Query Classification with Deep Neural NetworksCompanion Proceedings of the Web Conference 202010.1145/3366424.3382183(324-330)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3382183
Samuel ASharma D(2018)Location estimation of non-geo-tagged tweetsEvolutionary Intelligence10.1007/s12065-018-0163-3Online publication date: 14-Aug-2018
https://doi.org/10.1007/s12065-018-0163-3
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Index Terms

Using temporal profiles of queries for precision prediction
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo J�rvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2004

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SIGIR04

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Vuong TJacucci GRuotsalo T(2024)Naturalistic Digital Behavior Predicts Cognitive AbilitiesACM Transactions on Computer-Human Interaction10.1145/366034131:3(1-32)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3660341
Gandhi SMansouri BCampos RJatowt A(2020)Event-Related Query Classification with Deep Neural NetworksCompanion Proceedings of the Web Conference 202010.1145/3366424.3382183(324-330)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3382183
Samuel ASharma D(2018)Location estimation of non-geo-tagged tweetsEvolutionary Intelligence10.1007/s12065-018-0163-3Online publication date: 14-Aug-2018
https://doi.org/10.1007/s12065-018-0163-3
Zhang ZChen JWu S(2018)Query Performance Prediction and Classification for Information Search SystemsWeb and Big Data10.1007/978-3-319-96890-2_23(277-285)Online publication date: 19-Jul-2018
https://doi.org/10.1007/978-3-319-96890-2_23
Jiang DChen HCai F(2017)Exploiting Query’s Temporal Patterns for Query AutocompletionMathematical Problems in Engineering10.1155/2017/74908792017:1Online publication date: 23-Mar-2017
https://doi.org/10.1155/2017/7490879
Samuel ASharma D(2017)Summary generation using geo-coordinates and temporal data in microblogging environment2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT.2017.8204049(1-5)Online publication date: Jul-2017
https://doi.org/10.1109/ICCCNT.2017.8204049
Di Martino SRomano SBertolotto MKanhabua NMazzeo ANejdl W(2017)Towards Exploiting Social Networks for Detecting Epidemic OutbreaksGlobal Journal of Flexible Systems Management10.1007/s40171-016-0148-y18:1(61-71)Online publication date: 11-Jan-2017
https://doi.org/10.1007/s40171-016-0148-y
Cheng GHuang GDong CZhu JZhou XYao Y(2017) An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin, Canada River Research and Applications10.1002/rra.313633:5(823-843)Online publication date: 28-Feb-2017
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Cheng GHuang GDong CZhu JZhou XYao Y(2017)High‐resolution projections of 21st century climate over the Athabasca River Basin through an integrated evaluation‐classification‐downscaling‐based climate projection frameworkJournal of Geophysical Research: Atmospheres10.1002/2016JD026158122:5(2595-2615)Online publication date: 3-Mar-2017
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Romano SMartino SKanhabua NMazzeo ANejdl W(2016)Challenges in Detecting Epidemic Outbreaks from Social Networks2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2016.111(69-74)Online publication date: Mar-2016
https://doi.org/10.1109/WAINA.2016.111
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