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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Databases Information retrieval

• Full title: Recommender Systems
• Subtitle: Frontiers and Practices
• Author: Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie
• Language: English
• Binding: Hardback
• Number of pages: 296
• EAN: 9789819989638
• ID: 44429266
• Publisher: Springer, Berlin
• Weight: 555 g
• Dimensions: 235 × 155 mm
• Date of publishing: 11. May 2024

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