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000 nam5i
001 2210080933917
003 DE-He213
005 20250321105302
007 cr nn 008mamaa
008 240325s2024 si | s |||| 0|eng d
020 a97898199896459978-981-99-8964-5
024 a10.1007/978-981-99-8964-52doi
040 a221008
050 aQA75.5-76.95
072 aUNH2bicssc
072 aUND2bicssc
072 aCOM0300002bisacsh
072 aUNH2thema
072 aUND2thema
082 a025.04223
100 aLi, Dongsheng.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
245 00 aRecommender Systemsh[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 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
347 atext filebPDF2rda
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.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aZhang, Le.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aRen, Kan.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aLu, Tun.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aWu, Tao.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aXie, Xing.eauthor.4aut4http://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
Lian Jianxun. author Zhang Le. author Ren Kan. author Lu Tun. author Wu Tao. author Xie Xing. author
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.
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CClosed Stack Request
IInter-Campus Loan
CPriority Cataloging
PPrint
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