Article

Minimal document set retrieval

Authors:

Rohini SrihariAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 752 - 759

https://doi.org/10.1145/1099554.1099735

Published: 31 October 2005 Publication History

Abstract

This paper presents a novel formulation and approach to the minimal document set retrieval problem. Minimal Document Set Retrieval (MDSR) is a promising information retrieval task in which each query topic is assumed to have different subtopics; the task is to retrieve and rank relevant document sets with maximum coverage but minimum redundancy of subtopics in each set. For this task, we propose three document set retrieval and ranking algorithms: Novelty Based method, Cluster Based method and Subtopic Extraction Based method. In order to evaluate the system performance, we design a new evaluation framework for document set ranking which evaluates both relevance between set and query topic, and redundancy within each set. Finally, we compare the performance of the three algorithms using the TREC interactive track dataset. Experimental results show the effectiveness of our algorithms.

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Cited By

Câmara AMaxwell DHauff C(2022)Searching, Learning, and Subtopic Ordering: A Simulation-Based AnalysisAdvances in Information Retrieval10.1007/978-3-030-99736-6_10(142-156)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_10
Lee SPark J(2013)Topic based photo set retrieval using user annotated tagsMultimedia Tools and Applications10.1007/s11042-011-0850-x64:1(7-26)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1007/s11042-011-0850-x
Liu YAgah A(2009)A Prototype Process-Based Search EngineProceedings of the 2009 IEEE International Conference on Semantic Computing10.1109/ICSC.2009.8(481-486)Online publication date: 14-Sep-2009
https://dl.acm.org/doi/10.1109/ICSC.2009.8
Show More Cited By

Index Terms

Minimal document set retrieval
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Copyright © 2005 ACM.

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: 31 October 2005

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CIKM05

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October 31 - November 5, 2005

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Câmara AMaxwell DHauff C(2022)Searching, Learning, and Subtopic Ordering: A Simulation-Based AnalysisAdvances in Information Retrieval10.1007/978-3-030-99736-6_10(142-156)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_10
Lee SPark J(2013)Topic based photo set retrieval using user annotated tagsMultimedia Tools and Applications10.1007/s11042-011-0850-x64:1(7-26)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1007/s11042-011-0850-x
Liu YAgah A(2009)A Prototype Process-Based Search EngineProceedings of the 2009 IEEE International Conference on Semantic Computing10.1109/ICSC.2009.8(481-486)Online publication date: 14-Sep-2009
https://dl.acm.org/doi/10.1109/ICSC.2009.8
Liu YAgah A(2009)Query operations of process-based searches2009 Fourth International Conference on Digital Information Management10.1109/ICDIM.2009.5356786(1-6)Online publication date: Nov-2009
https://doi.org/10.1109/ICDIM.2009.5356786
Qin TLiu TZhang XWang DXiong WLi HHuai JChen RHon HLiu YMa WTomkins AZhang X(2008)Learning to rank relational objects and its application to web searchProceedings of the 17th international conference on World Wide Web10.1145/1367497.1367553(407-416)Online publication date: 21-Apr-2008
https://dl.acm.org/doi/10.1145/1367497.1367553
Boutemedjet SZiou D(2008)A Graphical Model for Context-Aware Visual Content RecommendationIEEE Transactions on Multimedia10.1109/TMM.2007.91122610:1(52-62)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1109/TMM.2007.911226
Li XCroft W(2008)An information-pattern-based approach to novelty detectionInformation Processing and Management: an International Journal10.1016/j.ipm.2007.09.01344:3(1159-1188)Online publication date: 1-May-2008
https://dl.acm.org/doi/10.1016/j.ipm.2007.09.013
Berlanga-Llavori RAnaya-Sánchez HPons-Porrata AJiménez-Ruiz E(2008)Conceptual Subtopic Identification in the Medical DomainAdvances in Artificial Intelligence – IBERAMIA 200810.1007/978-3-540-88309-8_32(312-321)Online publication date: 14-Oct-2008
https://dl.acm.org/doi/10.1007/978-3-540-88309-8_32
Li XCroft WYu PTsotras VFox ELiu B(2006)Improving novelty detection for general topics using sentence level information patternsProceedings of the 15th ACM international conference on Information and knowledge management10.1145/1183614.1183652(238-247)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1145/1183614.1183652

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