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Learning Apache Spark 2

You're reading from  Learning Apache Spark 2

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781785885136
Pages 356 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (18) Chapters

Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface
1. Architecture and Installation 2. Transformations and Actions with Spark RDDs 3. ETL with Spark 4. Spark SQL 5. Spark Streaming 6. Machine Learning with Spark 7. GraphX 8. Operating in Clustered Mode 9. Building a Recommendation System 10. Customer Churn Prediction 1. Theres More with Spark

Collaborative filtering

Most of us will have used eBay, Amazon, or any other popular web retailer. And most of us will have seen recommendations based on our past choices. For example, if you buy an electric toothbrush, Amazon would recommend you some extra brush heads as these items normally go together. All of these suggestions are based on recommended systems, which are typically based on collaborative filtering techniques.

Collaborative filtering algorithms recommend items (this is the filtering part) based on preference information from many users (this is the collaborative part). The collaborative filtering approach is based on similarity; the basic idea is people who liked similar items in the past will like similar items in the future.

In the following example, Adrian likes the movies Mission Impossible, Skyfall, Casino Royale, and Spectre. Bob likes the movies Skyfall, Casino Royale, and Spectre. Andrew likes the movies Skyfall and Spectre.

To recommend a movie to Andrew, we calculate...

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