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You're reading from Java Deep Learning Projects Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Length 436 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Deep Learning

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Getting Started with Deep Learning FREE CHAPTER

2. Cancer Types Prediction Using Recurrent Type Networks

3. Multi-Label Image Classification Using Convolutional Neural Networks

4. Sentiment Analysis Using Word2Vec and LSTM Network

5. Transfer Learning for Image Classification

6. Real-Time Object Detection using YOLO, JavaCV, and DL4J

7. Stock Price Prediction Using LSTM Network

8. Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks

9. Playing GridWorld Game Using Deep Reinforcement Learning

10. Developing Movie Recommendation Systems Using Factorization Machines

11. Discussion, Current Trends, and Outlook

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Index

Chapter 10. Developing Movie Recommendation Systems Using Factorization Machines

Factorization machines (FM) are a set of algorithms that enhance the performance of linear models by incorporating second-order feature interactions that are absent in matrix factorization (MF) algorithms in a supervised way. Therefore, FMs are very robust compared to their classical counterpart—collaborative filtering (CF)—and are gaining popularity in personalization and recommendation systems because they can be used to discover latent features underlying the interactions between two different kinds of entities.

In this chapter, we will develop a sample project for predicting both the rating and ranking to show their effectiveness. Nevertheless, we will see some theoretical background of recommendation systems using MF and CF before diving into the project's implementation using RankSys library-based FMs. In summary, the following topics will be covered in this chapter: