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Scala Machine Learning Projects

You're reading from Scala Machine Learning Projects

Product type Book

Published in Jan 2018

Publisher Packt

ISBN-13 9781788479042

Pages 470 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Analyzing Insurance Severity Claims

2. Analyzing and Predicting Telecommunication Churn

3. High Frequency Bitcoin Price Prediction from Historical and Live Data

4. Population-Scale Clustering and Ethnicity Prediction

5. Topic Modeling - A Better Insight into Large-Scale Texts

6. Developing Model-based Movie Recommendation Engines

7. Options Trading Using Q-learning and Scala Play Framework

8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks

9. Fraud Analytics Using Autoencoders and Anomaly Detection

10. Human Activity Recognition using Recurrent Neural Networks

11. Image Classification using Convolutional Neural Networks

1. Other Books You May Enjoy

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Index

Chapter 6. Developing Model-based Movie Recommendation Engines

Netflix is an American entertainment company founded by Reed Hastings and Marc Randolph on August 29, 1997, in Scotts Valley, California. It specializes in and provides streaming media, video-on-demand online, and DVD by mail. In 2013, Netflix expanded into film and television production, as well as online distribution. Netflix uses a model-based collaborative filtering approach for real-time movie recommendation for its subscribers.

In this chapter, we will see two end-to-end projects and develop a model for item-based collaborative filtering for movie similarity measurement and a model-based movie recommendation engine with Spark that recommends movies for new users. We will see how to interoperate between ALS and matrix factorization (MF) for these two scalable movie recommendation engines. We will use the movie lens dataset for the project. Finally, we will see how to deploy the best model in production.

In a nutshell, we will...

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