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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

Decision trees

Decision trees are a simple, fast, tree-based supervised learning algorithm to solve classification problems. Though not very accurate when compared to other logistic regression methods, this algorithm comes in handy while dealing with recommender systems.

We define the decision trees with an example. Imagine a situation where you have to predict the class of flower based on its features such as petal length, petal width, sepal length, and sepal width. We will apply the decision tree methodology to solve this problem:

  1. Consider the entire data at the start of the algorithm.

  2. Now, choose a suitable question/variable to divide the data into two parts. In our case, we chose to divide the data based on petal length > 2.45 and <= 2.45. This separates flower class setosa from the rest of the classes.

  3. Now, further divide the data having petal length >2.45, based on the same variable with petal length < 4.5 and >= 4.5, as shown in the following image.

  4. This splitting of the...

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