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Practical Machine Learning: Innovations in Recommendation

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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 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 recommenders Collect user data that tracks user actions—rather than their ratings Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis Use search technology to offer recommendations in real time, complete with item metadata Watch the recommender in action with a music service example Improve your recommender with dithering, multimodal recommendation, and other techniques


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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 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 recommenders Collect user data that tracks user actions—rather than their ratings Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis Use search technology to offer recommendations in real time, complete with item metadata Watch the recommender in action with a music service example Improve your recommender with dithering, multimodal recommendation, and other techniques

30 review for Practical Machine Learning: Innovations in Recommendation

  1. 5 out of 5

    Luke

    This is a very high level overview - nothing in depth. You can skim read this in less than a couple of hours. If you have next to no technical knowledge and have barely heard of a recommender system, then this is the book for you. Otherwise, I'd suggest saving your time by not reading this. This is a very high level overview - nothing in depth. You can skim read this in less than a couple of hours. If you have next to no technical knowledge and have barely heard of a recommender system, then this is the book for you. Otherwise, I'd suggest saving your time by not reading this.

  2. 5 out of 5

    Mike Fowler

    Interesting concepts but not much depth in explanations.

  3. 5 out of 5

    Tathagat Varma

    Discusses some foundational ideas in reocmmendation.

  4. 4 out of 5

    Siarhei Krukau

    This short book feels like an excerpt of a one-day technical course, “Machine Learning with Apache Mahout: Introduction to Scalable ML for Developers” that they promote on the last pages.

  5. 5 out of 5

    Taro

    A very high-level overview of how a recommendation system could be built with a Hadoop-based batch processing with a Lucene-based search API. I think the dependence on Hadoop, which simply isn't easy to manage for most development teams, makes this an obsolete recommendation. A very high-level overview of how a recommendation system could be built with a Hadoop-based batch processing with a Lucene-based search API. I think the dependence on Hadoop, which simply isn't easy to manage for most development teams, makes this an obsolete recommendation.

  6. 5 out of 5

    Daron Yondem

    This was indeed practical. It's a very short read providing a lot of insights and tricks in ML for a newbie. This was indeed practical. It's a very short read providing a lot of insights and tricks in ML for a newbie.

  7. 5 out of 5

    Randy

    Really short book. Sort of helpful to read a mention of a concept one more time, and the engine idea is ok. I'd only recommend the book because it's so short it wouldn't take up much time. Really short book. Sort of helpful to read a mention of a concept one more time, and the engine idea is ok. I'd only recommend the book because it's so short it wouldn't take up much time.

  8. 5 out of 5

    B.

    Short and complete introduction to a recommendation system with machine learning. It is great as a first lecture in the field.

  9. 4 out of 5

    Niraj Prasad

  10. 5 out of 5

    Anandasubramanian

  11. 4 out of 5

    Bill Metangmo

  12. 5 out of 5

    Gaurav Goyal

  13. 5 out of 5

    Suhan

  14. 5 out of 5

    Jacob

  15. 4 out of 5

    Pamir Erdem

  16. 5 out of 5

    Anup

  17. 4 out of 5

    Subhajit Das

  18. 5 out of 5

    David Lacy

  19. 4 out of 5

    Irina

  20. 5 out of 5

    Mohit

  21. 4 out of 5

    Gagan Bajpai

  22. 5 out of 5

    Monica Kothari

  23. 4 out of 5

    Michelle Tran

  24. 5 out of 5

    Subin

  25. 4 out of 5

    Assaad

  26. 4 out of 5

    Mallik GS

  27. 4 out of 5

    Ritwik Paul

  28. 4 out of 5

    Rajan Pathak

  29. 4 out of 5

    Arunraj Nair

  30. 4 out of 5

    Brian

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