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Añadir al carritoPaperback. Condición: New. 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 settings--and 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. You'll 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 actions--rather 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.
Idioma: Inglés
Publicado por O'Reilly Media 10/6/2014, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
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Añadir al carritoPaperback or Softback. Condición: New. Practical Machine Learning: Innovations in Recommendation. Book.
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Añadir al carritoPaperback. Condición: New. 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 settings--and 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. You'll 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 actions--rather 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.
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Idioma: Inglés
Publicado por O'Reilly Media, Inc, USA, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
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Idioma: Inglés
Publicado por Oreilly & Associates Inc, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
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Añadir al carritoCondición: New. 2016. Paperback. . . . . .
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Añadir al carritoCondición: New. 2016. Paperback. . . . . . Books ship from the US and Ireland.
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 18,41
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Añadir al carritoPaperback. Condición: New. 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 settings--and 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. You'll 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 actions--rather 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.
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Añadir al carritoKartoniert / Broschiert. Condición: New. Über den AutorrnrnTed Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, ZooKeeper, and Drill projects and mentor for the Apache Storm, DataFu, Flink, and Optiq projects. .
Idioma: Inglés
Publicado por O'reilly Media Nov 2014, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 23,18
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - 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.
Librería: Rarewaves.com UK, London, Reino Unido
EUR 19,14
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Añadir al carritoPaperback. Condición: New. 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 settings--and 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. You'll 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 actions--rather 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.