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240403 ||| eng |
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|a 9789819989645
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| 100 |
1 |
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|a Li, Dongsheng
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| 245 |
0 |
0 |
|a Recommender Systems
|h Elektronische Ressource
|b Frontiers and Practices
|c by Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie
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| 250 |
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|a 1st ed. 2024
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2024, 2024
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| 300 |
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|a XVI, 280 p. 92 illus., 75 illus. in color
|b online resource
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| 505 |
0 |
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|a 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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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Information Storage and Retrieval
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| 653 |
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|a Data mining
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| 653 |
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|a Information storage and retrieval systems
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Data Mining and Knowledge Discovery
|
| 700 |
1 |
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|a Lian, Jianxun
|e [author]
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| 700 |
1 |
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|a Zhang, Le
|e [author]
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| 700 |
1 |
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|a Ren, Kan
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
|
| 028 |
5 |
0 |
|a 10.1007/978-981-99-8964-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-99-8964-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 025.04
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| 520 |
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|a 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.
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