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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Designing the content-based recommendation engine

To rewrite our customer requirements in plain English: When a customer browses a particular article, what other articles should we suggest to him?

Let's quickly recap how a content-based recommendation engine works. When a user is browsing a product or item, we need to provide recommendations to the user in the form of other products or items from our catalog. We can use the properties of the items to come up with the recommendations. Let's translate this to our use case.

Items in our case, are news articles.

The properties of a news article are as follows:

  • Its content, stored in a text column
  • The publisher--who published the article
  • The category to which the article belongs

So when a user is browsing a particular news article, we need to give him other news articles as recommendations, based on:

  • The text content...
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