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You're reading from  Clojure for Data Science

Product typeBook
Published inSep 2015
Reading LevelIntermediate
Publisher
ISBN-139781784397180
Edition1st Edition
Languages
Clojure
Concepts
Data Analysis
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Author (1)
Henry Garner
Henry Garner
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Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
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Building a user-based recommender with Mahout

The Mahout library comes with a lot of built-in classes, which are designed to work together to assist in building custom recommendation engines. Mahout's functionality to construct recommenders is in the org.apache.mahout.cf.taste namespace.

Note

Mahout's recommendation engine capabilities come from the Taste open source project with which it merged in 2008.

In the previous chapter, we discovered how to make use of Mahout to cluster with Clojure's Java interop capabilities. In this chapter, we'll make use of Mahout's recommenders with GenericUserBasedRecommender available in the org.apache.mahout.cf.taste.impl.recommender package.

As with many user-based recommenders, we also need to define a similarity metric to quantify how alike two users are. We'll also define a user neighborhood as each user's set of 10 most similar users.

First, we must load the data. Mahout includes a utility class, FileDataModel, to load the MovieLens data in the org.apache...

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Clojure for Data Science
Published in: Sep 2015Publisher: ISBN-13: 9781784397180
© 2015 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner

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