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by Suresh K. Gorakala
Building a Recommendation System with R
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Building a Recommendation System with R
Table of Contents
Building a Recommendation System with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Citation
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. 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
Solving a data analysis problem
Data preprocessing techniques
Similarity measures
Euclidian distance
Cosine distance
Pearson correlation
Dimensionality reduction
Principal component analysis
Data mining techniques
Cluster analysis
Explaining the k-means cluster algorithm
Support vector machine
Decision trees
Ensemble methods
Bagging
Random forests
Boosting
Evaluating data-mining algorithms
Summary
3. Recommender Systems
R package for recommendation – recommenderlab
Datasets
Jester5k, MSWeb, and MovieLense
The class for rating matrices
Computing the similarity matrix
Recommendation models
Data exploration
Exploring the nature of the data
Exploring the values of the rating
Exploring which movies have been viewed
Exploring the average ratings
Visualizing the matrix
Data preparation
Selecting the most relevant data
Exploring the most relevant data
Normalizing the data
Binarizing the data
Item-based collaborative filtering
Defining the training and test sets
Building the recommendation model
Exploring the recommender model
Applying the recommender model on the test set
User-based collaborative filtering
Building the recommendation model
Applying the recommender model on the test set
Collaborative filtering on binary data
Data preparation
Item-based collaborative filtering on binary data
User-based collaborative filtering on binary data
Conclusions about collaborative filtering
Limitations of collaborative filtering
Content-based filtering
Hybrid recommender systems
Knowledge-based recommender systems
Summary
4. Evaluating the Recommender Systems
Preparing the data to evaluate the models
Splitting the data
Bootstrapping data
Using k-fold to validate models
Evaluating recommender techniques
Evaluating the ratings
Evaluating the recommendations
Identifying the most suitable model
Comparing models
Identifying the most suitable model
Optimizing a numeric parameter
Summary
5. Case Study – Building Your Own Recommendation Engine
Preparing the data
Description of the data
Importing the data
Defining a rating matrix
Extracting item attributes
Building the model
Evaluating and optimizing the model
Building a function to evaluate the model
Optimizing the model parameters
Summary
A. References
Index
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