Recommender Systems
Frontiers and Practices
Parameter
- 296 Seiten
- 11 Lesestunden
Mehr zum Buch
This book starts from the classic recommendation algorithm, introduces readers to the basic principles and main concepts of this traditional algorithm, and analyzes its 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 recommendation 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 recommendation systems from scratch.This book is suitable not only for technical personnel in related fields such as the Internet and big data, but also for undergraduate and graduate students majoring in computer science, software engineering, and artificial intelligence.
Buchkauf
Recommender Systems, Tao Wu, Le Zhang, Dongsheng Li, Jianxun Lian, Tun Lu, Kan Ren, Xing Xie
- Sprache
- Erscheinungsdatum
- 2024
- Einband
- (Hardcover),
- Buchzustand
- Gebraucht - Gut
- Preis
- € 46,99
Keiner hat bisher bewertet.
- Titel
- Recommender Systems
- Untertitel
- Frontiers and Practices
- Sprache
- Englisch
- Autor*innen
- Tao Wu, Le Zhang, Dongsheng Li, Jianxun Lian, Tun Lu, Kan Ren, Xing Xie
- Verlag
- Springer
- Erscheinungsdatum
- 2024
- Einband
- Hardcover
- Seitenzahl
- 296
- ISBN10
- 9819989639
- ISBN13
- 9789819989638
- Reihe
- Kategorien
- Schlagwörter
- Künstliche Intelligenz, Algorithmen, Maschinelles Lernen, Expertensysteme
- Beschreibung
- This book starts from the classic recommendation algorithm, introduces readers to the basic principles and main concepts of this traditional algorithm, and analyzes its 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 recommendation 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 recommendation systems from scratch.This book is suitable not only for technical personnel in related fields such as the Internet and big data, but also for undergraduate and graduate students majoring in computer science, software engineering, and artificial intelligence.