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Reciprocal recommender systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reciprocal recommender systemsby James Neve.
Author:	Neve, James.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xi, 107 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Recommender systems (Information filtering)
Online resource:	https://doi.org/10.1007/978-3-031-85103-2
ISBN:	9783031851032$q(electronic bk.)

Reciprocal recommender systems
Neve, James.

Reciprocal recommender systems[electronic resource] /by James Neve. - Cham :Springer Nature Switzerland :2025. - xi, 107 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Preface -- 1. Introduction -- 2. Theoretical Background -- 3. Collaborative Filtering -- 4. Content-Based Filtering -- 5. Hybrid Filtering and Additional Approaches -- 6. Matching Theory -- 7. Ethical Concerns and Future Work.

This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successful algorithms in the field. Researchers and developers with a little background in machine learning will find many of the algorithms are straightforward to implement, and code samples are included to help with this. In addition to accessible algorithms, the book also examines some more cutting-edge research such as the recent interest in applying matching theory to reciprocal recommendation. These parts will be of interest both to developers who are looking to optimize their systems, and to researchers who might find avenues to further advance the field and develop new methods of recommending people to people. By the end of this book, the reader will have a comprehensive understanding of the state of the art in reciprocal recommendation and will be equipped to design and implement their own systems.

ISBN: 9783031851032$q(electronic bk.)

Standard No.: 10.1007/978-3-031-85103-2doiSubjects--Topical Terms:

310886
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 005.56

Reciprocal recommender systems
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520 $a This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successful algorithms in the field. Researchers and developers with a little background in machine learning will find many of the algorithms are straightforward to implement, and code samples are included to help with this. In addition to accessible algorithms, the book also examines some more cutting-edge research such as the recent interest in applying matching theory to reciprocal recommendation. These parts will be of interest both to developers who are looking to optimize their systems, and to researchers who might find avenues to further advance the field and develop new methods of recommending people to people. By the end of this book, the reader will have a comprehensive understanding of the state of the art in reciprocal recommendation and will be equipped to design and implement their own systems.
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