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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Introduction


The following is Wikipedia's definition of recommender systems:

"Recommender systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item."

Recommender systems have gained immense popularity in recent years. Amazon uses them to recommend books, Netflix for movies, and Google News to recommend news stories. As the proof is in the pudding, here are some examples of the impact recommendations can have (source: Celma, Lamere, 2008):

  • Two-thirds of the movies watched on Netflix are recommended

  • 38 percent of the news clicks on Google News are recommended

  • 35 percent of the sales at Amazon sales are the result of recommendations

As we have seen in the previous chapters, features and feature selection play a major role in the efficacy of machine learning algorithms. Recommender engine algorithms discover these features, called latent features, automatically. In short, there are latent features responsible for a user...

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