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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

Ensemble methods

In data mining, we use ensemble methods, which means using multiple learning algorithms to obtain better predictive results than applying any single learning algorithm on any statistical problem. This section will provide an overview of popular ensemble methods such as bagging, boosting, and random forests

Bagging

Bagging is also known as Bootstrap aggregating. It is designed to improve the stability and accuracy of machine-learning algorithms. It helps avoid over fitting and reduces variance. This is mostly used with decision trees.

Bagging involves randomly generating Bootstrap samples from the dataset and trains the models individually. Predictions are then made by aggregating or averaging all the response variables:

  • For example, consider a dataset (Xi, Yi), where i=1 …n, contains n data points.

  • Now, randomly select B samples with replacements from the original dataset using Bootstrap technique.

  • Next, train the B samples with regression/classification models independently....

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