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Live visual relevance feedback for query formulation

Published: 15 August 2005 Publication History

Abstract

Users browsing the Internet seem relatively satisfied with the performance of search engines. An optimistic explanation would be the high quality of search engines. A more pessimistic one would be that people just adapt easily to any new technology. A third explanation is people's ignorance about recall: as they simply don't know what relevant documents are missed, they can hardly be expected to worry about them. And so they easily conceive the result as the best they can get. To allow the user to better assess the quality of the search results, an algorithm was developed that computes a visual representation of the document space in the neighborhood of the user's query.The paper (1) outlines the algorithm, (2) shows how users can explore the neighborhood of a query, and (3) demonstrates how users can guess more judiciously whether they need to further elaborate their query to improve retrieval results.

References

[1]
M. W. Berry and R. D. Fierro. Low-rank orthogonal decompositions for information retrieval applications. Numerical linear algebra with applications, 3(4):301--327, 1996.
[2]
E. Hoenkamp. Unitary operators on the document space. Journal of the American Society for Information Science and Technology, 54(4):314--320, 2003.

Cited By

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  • (2019)TRIVIRProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345401(1-10)Online publication date: 23-Sep-2019
  • (2012)Taming the Terabytes: A Human‐Centered Approach to Surviving the Information DelugeInformation Overload10.1002/9781118360491.ch8(145-174)Online publication date: 15-Oct-2012
  • (2009)On the Notion of "An Information Need"Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory10.1007/978-3-642-04417-5_38(354-357)Online publication date: 3-Sep-2009

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  1. Live visual relevance feedback for query formulation

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    cover image ACM Conferences
    SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
    August 2005
    708 pages
    ISBN:1595930345
    DOI:10.1145/1076034
    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: 15 August 2005

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    Author Tags

    1. query formulation
    2. real-time
    3. visualization

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

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    Cited By

    View all
    • (2019)TRIVIRProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345401(1-10)Online publication date: 23-Sep-2019
    • (2012)Taming the Terabytes: A Human‐Centered Approach to Surviving the Information DelugeInformation Overload10.1002/9781118360491.ch8(145-174)Online publication date: 15-Oct-2012
    • (2009)On the Notion of "An Information Need"Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory10.1007/978-3-642-04417-5_38(354-357)Online publication date: 3-Sep-2009

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