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Recommender Systems Frontiers and Practices
Author
Title
Recommender Systems [electronic resource] : Frontiers and Practices / by Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie.
ISBN
9789819989645
Edition
1st ed. 2024.
Publication
Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Physical Description
1 online resource (XVI, 280 p.) 92 illus., 75 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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. .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 10, 2024
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.
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