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abstract

LSDS-IR'15: 2015 Workshop on Large-Scale and Distributed Systems for Information Retrieval

Published: 17 October 2015 Publication History

Abstract

The growth of the Web and other Big Data sources lead to important performance problems for large-scale and distributed information retrieval systems. The scalability and efficiency of such information retrieval systems have an impact on their effectiveness, eventually affecting the experience of their users and monetization as well. The LSDS-IR'15 workshop will provide space for researchers to discuss the existing performance problems in the context of large-scale and distributed information retrieval systems and define new research directions in the modern Big Data era. The workshop expects to bring together information retrieval practitioners from the industry, as well as academic researchers concerned with any aspect of large-scale and distributed information retrieval systems.

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  1. LSDS-IR'15: 2015 Workshop on Large-Scale and Distributed Systems for Information Retrieval

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    cover image ACM Conferences
    CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
    October 2015
    1998 pages
    ISBN:9781450337946
    DOI:10.1145/2806416
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 October 2015

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    Author Tags

    1. distributed information retrieval
    2. efficiency
    3. large-scale information retrieval
    4. performance
    5. web search

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    CIKM'15
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    Acceptance Rates

    CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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