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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

Chapter 9. Recommendation Systems Using Factorization Machines

Factorization models are very popular in 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 provide several examples of how to develop recommendation system for predictive analytics.

We will see the theoretical background of recommendation systems, such as matrix factorization. Later in the chapter, we will see how to use a collaborative approach to develop a movie recommendation system. Finally, will see how to use Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems.

In summary, the following topics will be covered in this chapter:

  • Recommendation systems
  • A movie recommendation system using the collaborative filtering approach
  • K-means for clustering similar movies
  • FM-based recommendation systems
  • Using improved FMs for movie recommendation
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