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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from  Frank Kane's Taming Big Data with Apache Spark and Python

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Pages 296 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Frank Kane Frank Kane
Profile icon Frank Kane
LinkedIn
Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
See other products by Frank Kane

Table of Contents (13) Chapters

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Spark Spark Basics and Spark Examples Advanced Examples of Spark Programs Running Spark on a Cluster SparkSQL, DataFrames, and DataSets Other Spark Technologies and Libraries Where to Go From Here? – Learning More About Spark and Data Science

Creating similar movies from one million ratings – part 3

About 15 minutes after I set off our movie-similarities-1m script on a cluster using EMR, I have some actual results to look at. Let's review what happened.

Assessing the results

Here are the results:

The top similar movie to Star Wars Episode Four, was Star Wars Episode Five, not too surprising. But the next entry is a little bit surprising, some little movie called Sanjuro had a very high similarity score. Let's look at what's going on there. Its actual strength, the number of people that rated that together with Star Wars, was only 60, so I think it's safe to say that is kind of a spurious result. Now that we're using a million ratings, we probably need to increase that minimum threshold on the number of co-raters in order to actually display a result. By doing so, we could probably pretty easily filter out movies like that and instead get Raiders of the Lost Ark as our second result instead of as our third. I think the position...

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Authors (1)

Profile icon Frank Kane
LinkedIn
Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
See other products by Frank Kane

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