Practical Machine Learning

by Ted Dunning, Ellen Friedman

Building a simple but powerful recommendation system is much easier than you think. 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 recommender. The style of the report makes this subject approachable for all levels of expertise.

Authors 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 Apache Mahout library, and then easily deploy the recommender using search technology with Apache Solr. This powerful and effective combination is efficient: it 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 Mahout for co-occurrence analysisUse Solr 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

Genres: Nonfiction, Technology, Ebooks, Computers, Technical

ebook

First published April 16, 2014

Reviews

[Luke · Rating 2 out of 5]

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.

[Siarhei · Rating 1 out of 5]

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.

[Mike Fowler · Rating 3 out of 5]

Interesting concepts but not much depth in explanations.

[Dr. Tathagat Varma · Rating 2 out of 5]

Discusses some foundational ideas in reocmmendation.

[James Tomasino · Rating 1 out of 5]

All but useless high level info.

[ZeV · Rating 1 out of 5]

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.

[Daron Yondem · Rating 4 out of 5]

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

[B. · Rating 4 out of 5]

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

[Randy · Rating 2 out of 5]

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.