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Learning Social Media Analytics with R

You're reading from   Learning Social Media Analytics with R Transform data from social media platforms into actionable business insights

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787127524
Length 394 pages
Edition 1st Edition
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Authors (4):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
Karthik Ganapathy Karthik Ganapathy
Author Profile Icon Karthik Ganapathy
Karthik Ganapathy
Tushar Sharma Tushar Sharma
Author Profile Icon Tushar Sharma
Tushar Sharma
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Social Media Analytics 2. Twitter – What's Happening with 140 Characters FREE CHAPTER 3. Analyzing Social Networks and Brand Engagements with Facebook 4. Foursquare – Are You Checked in Yet? 5. Analyzing Software Collaboration Trends I – Social Coding with GitHub 6. Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange 7. Believe What You See – Flickr Data Analysis 8. News – The Collective Social Media! Index

Recommendation engine – let's open a restaurant


An important reason for including a recommendation engine as one of our use cases is to emphasize how we can link the existing data to frame it as an analytics problem and then to use existing solutions to solve it reliably. We don't expect to have cutting edge accuracy with this recommendation engine but we use it to give you a healthy learning of working on such problems as and when they arrive. Before we dive into building a recommendation engine, a (very) brief introduction is appropriate.

Recommendation engine – the clichés

Recommendation engines (or systems) are one of the most recognized machine learning applications in the industry today. To say that a lot of people equate recommendation engines to machine learning won't be an exaggeration. They command such immense visibility for a simple reason: they integrate with business and they work. A recommendation engine is ubiquitous in today's technology landscape. They are present on e-commerce...

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