TY  - BOOK
AU  - Li,Dongsheng
AU  - Lian,Jianxun
AU  - Zhang,Le
AU  - Ren,Kan
AU  - Lu,Tun
AU  - Wu,Tao
AU  - Xie,Xing
ED  - SpringerLink (Online service)
TI  - Recommender Systems: Frontiers and Practices 
SN  - 9789819989645
AV  - QA75.5-76.95 
U1  - 025.04 23
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Information storage and retrieval systems
KW  - Data mining
KW  - Artificial intelligence
KW  - Information Storage and Retrieval
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-99-8964-5
ER  -