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R Machine Learning By Example

You're reading from  R Machine Learning By Example

Product type Book
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Pages 340 pages
Edition 1st Edition
Languages
Author (1):
Mr. Raghav Bali Mr. Raghav Bali
Author Profile Icon Mr. Raghav Bali
Mr. Raghav Bali
Toc

Table of Contents (15) Chapters Close

R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
1. Getting Started with R and Machine Learning 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Collaborative filters


Recommendation systems and collaborative filters share a long history. From the early days of primitive recommender engines which utilized specific categorizations with hard-coded results, to current sophisticated recommender engines on various e-commerce platforms, recommender engines have made use of collaborative filters throughout. They are not only easy to understand but are equally simple to implement. Let us take this opportunity to learn more about collaborative filters before we dive into implementation details.

Note

Fun Fact

Recommender engines surely outdate any known e-commerce platform! Grundy, a virtual librarian, was developed in 1979. It was a system for recommending books to users. It modeled the users based upon certain pre-defined stereotypes and recommended books from a known list for each such category.

Core concepts and definitions

Collaborative filters (denoted as CF henceforth) and recommender engines in general use certain terms and definitions to...

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