| Description |
1 online resource (xix, 281 pages) |
| Contents |
Introduction -- Preliminaries -- Contextual and conceptual information retrieval -- Information retrieval models -- Evaluation of information retrieval systems -- Fundamentals of evolutionary algorithms -- Demand of evolutionary algorithms in information retrieval -- TABU annealing: an efficient and scalable strategy for document retrieval -- Efficient latent semantic indexing-based information retrieval framework using particle swarm optimization and simulated annealing -- Music-inspired optimization algorithm: harmony-TABU for document retrieval using rhetorical relations and relevance feedback -- Evaluation of light inspired optimization algorithm-based image retrieval -- An evolutionary approach for optimizing content-based image retrieval using support vector machine -- An application of Firefly algorithm to region-based image retrieval -- An evolutionary approach for optimization region-based image retrieval using support vector machine -- Optimization of sparse dictionary model for multimodal image summarization using Firefly algorithm -- A dynamic feature selection method for document ranking with relevance feedback approach -- TDCCREC: an efficient and scalable web-based recommendation system -- An automatic facet generation framework for document retrieval -- ASPDD: an efficient and scalable framework for duplication detection -- Improvisation of seeker satisfaction in Yahoo! community question answering using automatic ranking, abstract generation, and history updation -- Findings and summary of text information retrieval chapters -- Findings and summary of image retrieval and assessment of image mining systems chapters |
| Summary |
"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 mediaUsing 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"--Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Data mining.
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Querying (Computer science)
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Big data.
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Information retrieval -- Experiments
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Information storage and retrieval systems -- Evaluation
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Database searching.
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online searching.
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COMPUTERS -- General.
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Database searching
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Big data
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Data mining
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Information storage and retrieval systems -- Evaluation
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Querying (Computer science)
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| Form |
Electronic book
|
| ISBN |
9781315392608 |
|
1315392607 |
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9781315392622 |
|
1315392623 |
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9781315392615 |
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1315392615 |
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9781315392592 |
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1315392593 |
|
