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Practical machine learning : innovations in recommendation /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
©2014.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 141016s2014 caua ob 000 0 eng d | ||
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| 082 | 0 | 4 | |a 006.31 |q OCoLC |2 23/eng/20230216 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Dunning, Ted, |d 1956- | |
| 245 | 1 | 0 | |a Practical machine learning : |b innovations in recommendation / |c Ted Dunning, Ellen Friedman. |
| 260 | |a Sebastopol, CA : |b O'Reilly Media, |c ©2014. | ||
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |2 rda | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references. | ||
| 520 | 8 | |a Annotation |b Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. Youll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actionsrather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning |x Development. | |
| 650 | 0 | |a Machine learning |v Case studies. | |
| 650 | 6 | |a Apprentissage automatique |x Développement. | |
| 650 | 6 | |a Apprentissage automatique |v Études de cas. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 655 | 7 | |a Case studies. |2 fast |0 (OCoLC)fst01423765 | |
| 700 | 1 | |a Friedman, B. Ellen. | |
| 776 | 0 | 8 | |i Print version: |a Dunning, Ted. |t Practical machine learning. |b First edition |w (OCoLC)891382178 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781491915707/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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