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| 024 | ▼a10.1007/978-981-99-8964-5▼2doi | |
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| 100 | ▼aLi, Dongsheng.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 00 | ▼aRecommender Systems▼h[electronic resource] :▼bFrontiers and Practices /▼cby Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie. |
| 250 | ▼a1st ed. 2024. | |
| 264 | ▼aSingapore :▼bSpringer Nature Singapore :▼bImprint: Springer,▼c2024. | |
| 300 | ▼aXVI, 280 p. 92 illus., 75 illus. in color.▼bonline resource. | |
| 336 | ▼atext▼btxt▼2rdacontent | |
| 337 | ▼acomputer▼bc▼2rdamedia | |
| 338 | ▼aonline resource▼bcr▼2rdacarrier | |
| 347 | ▼atext file▼bPDF▼2rda | |
| 505 | ▼aChapter 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. | |
| 520 | ▼aThis 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. | |
| 650 | ▼aInformation storage and retrieval systems. | |
| 650 | ▼aData mining. | |
| 650 | ▼aArtificial intelligence. | |
| 650 | ▼aInformation Storage and Retrieval. | |
| 650 | ▼aData Mining and Knowledge Discovery. | |
| 650 | ▼aArtificial Intelligence. | |
| 700 | ▼aLian, Jianxun.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 700 | ▼aZhang, Le.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 700 | ▼aRen, Kan.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 700 | ▼aLu, Tun.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 700 | ▼aWu, Tao.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 700 | ▼aXie, Xing.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut | |
| 710 | ▼aSpringerLink (Online service) | |
| 773 | ▼tSpringer Nature eBook | |
| 776 | ▼iPrinted edition:▼z9789819989638 | |
| 776 | ▼iPrinted edition:▼z9789819989652 | |
| 776 | ▼iPrinted edition:▼z9789819989669 | |
| 856 | ▼uhttps://doi.org/10.1007/978-981-99-8964-5 | |
| 912 | ▼aZDB-2-SCS | |
| 912 | ▼aZDB-2-SXCS | |
| 950 | ▼aComputer Science (SpringerNature-11645) | |
| 950 | ▼aComputer Science (R0) (SpringerNature-43710) |
Recommender Systems[electronic resource] :Frontiers and Practices /by Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie
Material type
전자책
Title
Recommender Systems[electronic resource] :Frontiers and Practices /by Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, Xing Xie
Author's Name
판 사항
1st ed. 2024.
Physical Description
XVI, 280 p 92 illus, 75 illus in color online resource.
Keyword
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.
ISBN
관련 URL
Holdings Information
RReservation
MMissing Book Request
CClosed Stack Request
IInter-Campus Loan
CPriority Cataloging
PPrint
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