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Recommender systems = frontiers and practices /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recommender systems/ by Dongsheng Li ... [et al.].
Reminder of title:	frontiers and practices /
other author:	Li, Dongsheng.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xvi, 280 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.
Contained By:	Springer Nature eBook
Subject:	Recommender systems (Information filtering) -
Online resource:	https://doi.org/10.1007/978-981-99-8964-5
ISBN:	9789819989645

Recommender systems = frontiers and practices /
Recommender systemsfrontiers and practices /[electronic resource] :by Dongsheng Li ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xvi, 280 p. :ill., digital ;24 cm.

Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.

ISBN: 9789819989645

Standard No.: 10.1007/978-981-99-8964-5doiSubjects--Topical Terms:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 005.56

Recommender systems = frontiers and practices /
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260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xvi, 280 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.
520 $a This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
650 0 $a Recommender systems (Information filtering) $3 1002434
650 1 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Li, Dongsheng. $3 1915641
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-8964-5
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