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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem 2. Data Acquisition FREE CHAPTER 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Building a recommender

Now that we've explored our song analyzer, let's get back on track with the recommendation engine. As discussed earlier, we would like to recommend songs based on frequency hashes extracted from audio signals. Taking as an example the dispute between Led Zeppelin and Spirit, we would expect both songs to be relatively close to each other, as the allegation is that they share a melody. Using this thought as our main assumption, we could potentially recommend Taurus to someone interested in Stairway to Heaven.

The PageRank algorithm

Instead of recommending a specific song, we will recommend playlists. A playlist would consist of a list of all our songs ranked by relevance, most to least relevant. Let's begin with the assumption that people listen to music in a similar way to the way they browse articles on the web, that is, following a logical path from link to link, but occasionally switching direction, or teleporting, and browsing to a totally different...

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