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Recommender Systems [electronic resource] : Frontiers and Practices /

By: Contributor(s): Material type: TextTextPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XVI, 280 p. 92 illus., 75 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789819989645
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 025.04 23
LOC classification:
  • QA75.5-76.95
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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. .
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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. .

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