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The Notion of Relevance in Information Science [electronic resource] : Everybody knows what relevance is. But, what is it really? / by Tefko Saracevic.

By: Saracevic, Tefko [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Information Concepts, Retrieval, and Services: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XX, 109 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031023026.Subject(s): Computer networks  | Computer Communication NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 004.6 Online resources: Click here to access online
Contents:
Acknowledgments -- Preface -- Introduction -- A Bit of History -- Understanding, Manifestations, and Attributes -- Models of Relevance -- Theories of Relevance -- Experimental Studies on Behavior of Relevance -- Experimental Studies on Effects of Relevance -- Effects of Inconsistent Relevance Judgments on Information Retrieval Test Results -- Conclusions -- References -- Author Biography.
In: Springer Nature eBookSummary: Everybody knows what relevance is. It is a "ya'know" notion, concept, idea-no need to explain whatsoever. Searching for relevant information using information technology (IT) became a ubiquitous activity in contemporary information society. Relevant information means information that pertains to the matter or problem at hand-it is directly connected with effective communication. The purpose of this book is to trace the evolution and with it the history of thinking and research on relevance in information science and related fields from the human point of view. The objective is to synthesize what we have learned about relevance in several decades of investigation about the notion in information science. This book deals with how people deal with relevance-it does not cover how systems deal with relevance; it does not deal with algorithms. Spurred by advances in information retrieval (IR) and information systems of various kinds in handling of relevance, a number of basic questionsare raised: But what is relevance to start with? What are some of its properties and manifestations? How do people treat relevance? What affects relevance assessments? What are the effects of inconsistent human relevance judgments on tests of relative performance of different IR algorithms or approaches? These general questions are discussed in detail.
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Acknowledgments -- Preface -- Introduction -- A Bit of History -- Understanding, Manifestations, and Attributes -- Models of Relevance -- Theories of Relevance -- Experimental Studies on Behavior of Relevance -- Experimental Studies on Effects of Relevance -- Effects of Inconsistent Relevance Judgments on Information Retrieval Test Results -- Conclusions -- References -- Author Biography.

Everybody knows what relevance is. It is a "ya'know" notion, concept, idea-no need to explain whatsoever. Searching for relevant information using information technology (IT) became a ubiquitous activity in contemporary information society. Relevant information means information that pertains to the matter or problem at hand-it is directly connected with effective communication. The purpose of this book is to trace the evolution and with it the history of thinking and research on relevance in information science and related fields from the human point of view. The objective is to synthesize what we have learned about relevance in several decades of investigation about the notion in information science. This book deals with how people deal with relevance-it does not cover how systems deal with relevance; it does not deal with algorithms. Spurred by advances in information retrieval (IR) and information systems of various kinds in handling of relevance, a number of basic questionsare raised: But what is relevance to start with? What are some of its properties and manifestations? How do people treat relevance? What affects relevance assessments? What are the effects of inconsistent human relevance judgments on tests of relative performance of different IR algorithms or approaches? These general questions are discussed in detail.

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