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Data Fusion in Information Retrieval

  • Shengli¬†Wu

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 13)

Table of contents

  1. Front Matter
    Pages 1-11
  2. Shengli Wu
    Pages 1-5
  3. Shengli Wu
    Pages 7-18
  4. Shengli Wu
    Pages 19-42
  5. Shengli Wu
    Pages 43-71
  6. Shengli Wu
    Pages 73-116
  7. Shengli Wu
    Pages 117-133
  8. Shengli Wu
    Pages 135-147
  9. Shengli Wu
    Pages 181-212
  10. Back Matter
    Pages 0--1

About this book

Introduction

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others:

-          What are the key factors that affect the performance of data fusion algorithms significantly?

-          What conditions are favorable to data fusion algorithms?

-          CombSum and CombMNZ, which one is better? and why?

-          What is the rationale of using the linear combination method?

-          How can the best fusion option be found under any given circumstances?

Keywords

Data Fusion Digital Libraries Information Retrieval Meta-search

Authors and affiliations

  • Shengli¬†Wu
    • 1
  1. 1., School of Computing and MathematicsUniversity of UlsterNewtownabbeyUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28866-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-28865-4
  • Online ISBN 978-3-642-28866-1
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • Buy this book on publisher's site