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Information access in the era of generative AI

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information access in the era of generative AIedited by Ryen W. White, Chirag Shah.
other author:	White, Ryen W.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	ix, 248 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Information retrieval.
Online resource:	https://doi.org/10.1007/978-3-031-73147-1
ISBN:	9783031731471$q(electronic bk.)

Information access in the era of generative AI
Information access in the era of generative AI[electronic resource] /edited by Ryen W. White, Chirag Shah. - Cham :Springer Nature Switzerland :2025. - ix, 248 p. :ill. (chiefly color), digital ;24 cm. - Information retrieval series,v. 512730-6836 ;. - Information retrieval series ;v.37..

1. Introduction -- 2. Foundations of Generative IR -- 3. Interactions With Generative IR Systems -- 4. Adapting Generative IR Systems to Users, Tasks, and Scenarios -- 5. Improving Generative IR Systems Based on User Feedback -- 6. Generative IR Evaluation -- 7. Sociotechnical Implications of Generative AI for Information Access -- 8. Recommendation in the Era of Generative AI -- 9. Designing for the Future of Information Access with Generative IR.

Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people's personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction. The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, sociotechnical implications of GenAI, recommendations within GenIR, and the future of information access with GenIR. The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.

ISBN: 9783031731471$q(electronic bk.)

Standard No.: 10.1007/978-3-031-73147-1doiSubjects--Topical Terms:

215224
Information retrieval.

LC Class. No.: ZA3075

Dewey Class. No.: 025.524

Information access in the era of generative AI
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505 0 $a 1. Introduction -- 2. Foundations of Generative IR -- 3. Interactions With Generative IR Systems -- 4. Adapting Generative IR Systems to Users, Tasks, and Scenarios -- 5. Improving Generative IR Systems Based on User Feedback -- 6. Generative IR Evaluation -- 7. Sociotechnical Implications of Generative AI for Information Access -- 8. Recommendation in the Era of Generative AI -- 9. Designing for the Future of Information Access with Generative IR.
520 $a Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people's personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction. The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, sociotechnical implications of GenAI, recommendations within GenIR, and the future of information access with GenIR. The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.
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