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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
1. Getting Started with Spark 2. Spark Basics and Spark Examples 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Analyzing the ALS recommendations results

Open up Command Prompt and type spark-submit movie-recommendations-als.py with user 0:

That user 0 is my Star Wars fan that doesn't like Gone With The Wind.

Off it goes, using all the cores that I have. It should finish quite quickly. For such a fancy algorithm, that came back creepily fast, almost suspiciously so:

So, for my fictitious user who loves Star Wars and The Empire Strikes Back, but hated Gone With The Wind, the number one recommendations it produced was something called Love in the Afternoon and Roommates. What? What is this stuff? That's crazy. Lost in Space, okay, I can go with that, but the rest of this just doesn't make sense. What's worse is if I run it again, I'll actually get different results! Now, it could be that the algorithm is taking some shortcuts and randomly sampling things to save time, but even so, that's not good news.

Let's see what we get if we run it again. We get a totally different set of results:

There's something...

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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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