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| 001 | 978-981-99-8964-5 | ||
| 003 | DE-He213 | ||
| 005 | 20240423130330.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240325s2024 si | s |||| 0|eng d | ||
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_a10.1007/978-981-99-8964-5 _2doi |
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_aUNH _2bicssc |
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_a025.04 _223 |
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_aLi, Dongsheng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_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 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aXVI, 280 p. 92 illus., 75 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _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 | 0 | _aInformation storage and retrieval systems. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 1 | 4 | _aInformation Storage and Retrieval. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 700 | 1 |
_aLian, Jianxun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aZhang, Le. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aRen, Kan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aLu, Tun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aWu, Tao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aXie, Xing. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9789819989638 |
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_iPrinted edition: _z9789819989652 |
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_iPrinted edition: _z9789819989669 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-99-8964-5 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
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