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- Latest research on Data Fusion in Information Retrieval
- Includes various example applications such as developing more effective information retrieval systems, a more reliable comparison of retrieval results, the estimation of retrieval effectiveness, and world university ranking
- Written by a leading expert in the field
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 13)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
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?
Reviews
From the reviews:
“This book is … the result of a 10-year long engagement in data fusion within the context of various research projects. … The book is written in a very concise and dense manner, which makes it … readable for the expert, in particular the one with a good mathematical background. It contains a lot of evaluation results that help compare the various fusion methods presented, which is helpful for the practitioner. It also gives a good overview … of applications of data fusion.” (Gottfried Vossen, Zentralblatt MATH, Vol. 1246, 2012)
Authors and Affiliations
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, School of Computing and Mathematics, University of Ulster, Newtownabbey, United Kingdom
Shengli Wu
Bibliographic Information
Book Title: Data Fusion in Information Retrieval
Authors: Shengli Wu
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-642-28866-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-28865-4Published: 07 April 2012
Softcover ISBN: 978-3-642-44801-0Published: 09 May 2014
eBook ISBN: 978-3-642-28866-1Published: 05 April 2012
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: XII, 228
Topics: Computational Intelligence, Artificial Intelligence, Data Mining and Knowledge Discovery