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R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
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Author (1):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 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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