Recommender Systems : Frontiers and Practices / by Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie.

Format:

Book

Author/Creator:

Li, Dongsheng, 1955- author.

Language:

English

Subjects (All):

Information storage and retrieval systems.

Data mining.

Artificial intelligence.

Information Storage and Retrieval.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Local Subjects:

Information Storage and Retrieval.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Physical Description:

1 online resource (292 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

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.

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.

Notes:

Includes bibliographical references.

ISBN:

981-9989-64-7