Book Image

Scala Machine Learning Projects

Book Image

Scala Machine Learning Projects

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.
Table of Contents (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Spark-based movie recommendation systems


The implementation in Spark MLlib supports model-based collaborative filtering. In the model-based collaborative filtering technique, users and products are described by a small set of factors, also called LFs. In this section, we will see two complete examples of how it works toward recommending movies for new users.

Item-based collaborative filtering for movie similarity

Firstly, we read the ratings from a file. For this project, we can use the MovieLens 100k rating dataset from http://www.grouplens.org/node/73. The training set ratings are in a file called ua.base, while the movie item data is in u.item. On the other hand, ua.test contains the test set to evaluate our model. Since we will be using this dataset, we should acknowledge the GroupLens Research Project team at the University of Minnesota who wrote the following text:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive...