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Engineering - Computational Intelligence and Complexity | Data Fusion in Information Retrieval

Data Fusion in Information Retrieval

Wu, Shengli

2012, XII, 228 p.

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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

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?

Content Level » Research

Keywords » Data Fusion - Digital Libraries - Information Retrieval - Meta-search

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - Database Management & Information Retrieval

Table of contents 

Introduction.- Evaluation of Retrieval Results.- Score Normalization.- Observations and Analyses.- The Linear Combination Method.- A Geometric Framework for Data Fusion.- Ranking-Based Fusion.- Fusing Results from Overlapping Databases.- Application of the Data Fusion Technique.

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