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Deep Learning with TensorFlow. - Second Edition

You're reading from  Deep Learning with TensorFlow. - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Pages 484 pages
Edition 2nd Edition
Languages
Authors (2):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
1. Getting Started with Deep Learning 2. A First Look at TensorFlow 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 Index

Movie recommendation using collaborative filtering


In this section, we will see how to utilize collaborative filtering to develop a recommendation engine. However, before that let's discuss the utility matrix of preferences.

The utility matrix

In a collaborative filtering-based recommendation system, there are dimensions of entities: users and items (items refer to products, such as movies, games, and songs). As a user, you might have preferences for certain items. Therefore, these preferences must be extracted out of the data about items, users, or ratings. This data is often represented as a utility matrix, such as a user-item pair. This type of value can represent what is known about the degree of preference that the user has for a particular item.

The entry in the matrix can come from an ordered set. For example, integers 1-5 can be used to represent the number of stars that the user gave when rating items. We have already mentioned that users might not rate items very often, so most entries...

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