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R Machine Learning Projects

You're reading from  R Machine Learning Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Profile icon Dr. Sunil Kumar Chinnamgari

Table of Contents (12) Chapters

Preface 1. Exploring the Machine Learning Landscape 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
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Building a recommendation system with a user-based collaborative filtering technique

The Jokes recommendation system we built earlier, with item-based filtering, uses the powerful recommenderlab library available in R. In this implementation of the user-based collaborative filtering (UBCF) approach, we make use of the same library.

The following diagram shows the working principle of UBCF:

Example depicting working principle of user based collaborative filter

To understand the concept better, let's discuss the preceding diagram in detail. Let's assume that there are three users: X,Y, and Z. In UBCF, users X and Z are very similar as both of them like strawberries and watermelons. User X also likes grapes and oranges. So a user-based collaborative filter recommends grapes and oranges to user Z. The idea is that similar people tend to like similar things.

The primary...

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