Experiment and Evaluation in Information Retrieval Models explores different algorithms for the application of evolutionary computation to the field of information retrieval (IR). As well as examining existing approaches to resolving some of the problems in this field, results obtained by researchers are critically evaluated in order to give readers a clear view of the topic.

In addition, this book covers Algorithmic Solutions to the Problems in Advanced IR Concepts, including Feature Selection for Document Ranking, web page classification and recommendation, Facet Generation for Document Retrieval, Duplication Detection and seeker satisfaction in question answering community Portals.

Written with students and researchers in the field on information retrieval in mind, this book is also a useful tool for researchers in the natural and social sciences interested in the latest developments in the fast-moving subject area.

Key features:

Focusing on recent topics in Information Retrieval research, Experiment and Evaluation in Information Retrieval Models explores the following topics in detail:

  • Searching in social media
  • Using semantic annotations
  • Ranking documents based on Facets 
  • Evaluating IR systems offline and online
  • The role of evolutionary computation in IR
  • Document and term clustering,
  • Image retrieval
  • Design of user profiles for IR
  • Web page classification and recommendation
  • Relevance feedback approach for Document and image retrieval

section I|8 pages


chapter 1|6 pages


section II|48 pages


chapter 2|8 pages


chapter 4|12 pages

Information Retrieval Models

chapter 6|10 pages

Fundamentals of Evolutionary Algorithms

section III|32 pages

Demand of Evolutionary Algorithms in IR

section IV|82 pages

Model Formulations of Information Retrieval Techniques

section VI|20 pages

Findings and Summary