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Building a Recommendation System with R

Book Image

Building a Recommendation System with R

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Building a Recommendation System with R
Sep, 2015 | 158 pages
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Table of Contents (13 chapters)
Building a Recommendation System with R
Building a Recommendation System with R
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Recommender Systems
Getting Started with Recommender Systems
Understanding recommender systems
The structure of the book
Collaborative filtering recommender systems
Content-based recommender systems
Knowledge-based recommender systems
Hybrid systems
Evaluation techniques
A case study
The future scope
Summary
2
Data Mining Techniques Used in Recommender Systems
Data Mining Techniques Used in Recommender Systems
Solving a data analysis problem
Data preprocessing techniques
Data mining techniques
Cluster analysis
Decision trees
Ensemble methods
Evaluating data-mining algorithms
Summary
3
Recommender Systems
Recommender Systems
R package for recommendation – recommenderlab
Data exploration
Data preparation
Item-based collaborative filtering
User-based collaborative filtering
User-based collaborative filtering on binary data
Content-based filtering
Hybrid recommender systems
Knowledge-based recommender systems
Summary
4
Evaluating the Recommender Systems
Evaluating the Recommender Systems
Preparing the data to evaluate the models
Evaluating recommender techniques
Identifying the most suitable model
Summary
5
Case Study – Building Your Own Recommendation Engine
Case Study – Building Your Own Recommendation Engine
Preparing the data
Building the model
Evaluating and optimizing the model
Summary
References
References
Index
Index

Chapter 5. Case Study – Building Your Own Recommendation Engine

The previous two chapters showed how you how to build, test, and optimize recommender systems using R. Although the chapters were full of examples, they were based on datasets provided by an R package. The data was structured using redyal and was ready to be processed. However, in real life, the data preparation is an important, time-consuming, and tough step.

Another limitation of the previous examples is that they are based on the ratings only. In most of the situations, there are other data sources such as item descriptions and user profiles. A good solution comes from a combination of all the relevant information.

This chapter shows a practical example in which we will build and optimize a recommender system, starting from raw data. This chapter will cover the following topics:

  • Preparing the data to build a recommendation engine

  • Exploring the data through visualization techniques

  • Choosing and building a recommendation model

  • Optimizing...

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