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Practical Machine Learning: Innovations in Recommendation 1st Edition

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Ted Dunning (Author)
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Ted Dunning (Author), Ellen Friedman (Author)
3.1 out of 5 stars 7 customer reviews
ISBN-13: 978-1491915387
ISBN-10: 1491915382
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Product Details

  • Paperback: 56 pages
  • Publisher: O'Reilly Media; 1 edition (October 6, 2014)
  • Language: English
  • ISBN-10: 1491915382
  • ISBN-13: 978-1491915387
  • Product Dimensions: 6 x 0.1 x 9 inches
  • Shipping Weight: 3.2 ounces (View shipping rates and policies)
  • Average Customer Review: 3.1 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #2,574,979 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

3.0 out of 5 starsAn essay about ML and recommenders
By Dr Anton Chuvakin on June 20, 2014
Format: Kindle Edition Verified Purchase
I guess you can call it book but it is more of a long essay about ML and recommenders. I flipped thru the pages, and focused maybe on 10% of it - where there were some solid recommendations. It may make a good intro text to recommendations science
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4.0 out of 5 starsAccessible introduction to machine learning recommendation
By Erik Gfesser VINE VOICE on July 20, 2014
Format: Kindle Edition
This work is a short, white paper sized text that walks the reader through practical aspects of machine learning recommendation, specifically for those who are new to this space. After discussing the build versus buy dilemma with respect to incorporation of a recommendation system into the enterprise, the authors stress simplicity for ease of adoption and maintenance, and state that smart simplification in the case of recommendation is the focus of this paper, focusing on user behavior, co-occurrence, and text retrieval. Following this introductory discussion, the authors delve into a high-level discussion of choosing and collecting data for input into a recommendation system, building the recommendation model using co-occurrence, how Apache Mahout builds a model, and use of Apache Mahout with Apache Lucene (composed of Solr and Lucene-Core).

For readers already somewhat familiar with this problem space, it is not until Chapter 6 ("Example: Music Recommender") that the authors walk through an example that is used for a machine learning course developed by MapR Technologies. While still at a high level, this example discusses data sources, recommendations at scale using an Apache Hadoop based cluster, use of search to make recommendations, dithering, anti-flood measures, and multimodal and cross recommendation. While some technologists will likely be inclined to initiate work in this area by hitting community websites for the open source projects discussed, this book provides an accessible introduction to machine learning recommendation, and references to commercial implementations such as LucidWorks Search and the MapR distribution of Hadoop can be largely ignored.
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4.0 out of 5 starsQuick read for a good conceptual understanding (and overview of some relevant tools)
By Amazon Customer on December 18, 2014
Format: Kindle Edition Verified Purchase
A quick read which can give you a good grasp of concepts (as well as some tools) behind how a recommendation engine may be built. Found it to be very useful to start off in the area.
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1.0 out of 5 starsI was hoping to get information about different algorithms, ...
By mercedita jm villar on September 12, 2014
Format: Kindle Edition Verified Purchase
I was hoping to get information about different algorithms, and when they can be applied. Instead, I got information about a recommendation system.
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