book

Building a Recommendation System with R

Name: Building a Recommendation System with R
Author: Michele Usuelli
ISBN: 9781783554492

by Michele Usuelli

September 2015

Intermediate to advanced

158 pages

3h 9m

English

Packt Publishing

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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
Support files, eBooks, discount offers, and moreWhy subscribe?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 codeDownloading the color images of this bookErrataPiracyQuestions
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 measuresEuclidian distanceCosine distancePearson correlationDimensionality reductionPrincipal component analysis
Data mining techniques
Cluster analysis
Explaining the k-means cluster algorithmSupport vector machine
Decision trees
Ensemble methods
BaggingRandom forestsBoosting
Evaluating data-mining algorithms
Summary
3. Recommender Systems
R package for recommendation – recommenderlabDatasetsJester5k, MSWeb, and MovieLenseThe class for rating matricesComputing the similarity matrixRecommendation models
Data exploration
Exploring the nature of the dataExploring the values of the ratingExploring which movies have been viewedExploring the average ratingsVisualizing the matrix
Data preparation
Selecting the most relevant dataExploring the most relevant dataNormalizing the dataBinarizing the data
Item-based collaborative filtering
Defining the training and test setsBuilding the recommendation modelExploring the recommender modelApplying the recommender model on the test set
User-based collaborative filtering
Building the recommendation modelApplying the recommender model on the test setCollaborative filtering on binary dataData preparationItem-based collaborative filtering on binary data
User-based collaborative filtering on binary data
Conclusions about collaborative filteringLimitations 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 modelsSplitting the dataBootstrapping dataUsing k-fold to validate models
Evaluating recommender techniques
Evaluating the ratingsEvaluating the recommendations
Identifying the most suitable model
Comparing modelsIdentifying the most suitable modelOptimizing a numeric parameter
Summary
5. Case Study – Building Your Own Recommendation Engine
Preparing the dataDescription of the dataImporting the dataDefining a rating matrixExtracting item attributes
Building the model
Evaluating and optimizing the model
Building a function to evaluate the modelOptimizing the model parameters
Summary
A. References
Index

Content preview from Building a Recommendation System with R

Chapter 3. Recommender Systems

This chapter shows some popular recommendation techniques. In addition, we will implement some of them in R.

We will deal with the following techniques:

Collaborative filtering: This is the branch of techniques that we will explore in detail. The algorithms are based on information about similar users or similar items. The two sub-branches are as follows:
- Item-based collaborative filtering: This recommends to a user the items that are most similar to the user's purchases
- User-based collaborative filtering: This recommends to a user the items that are the most preferred by similar users
Content-based filtering: This is for each user; it defines a user profile and identify the items that match it.
Hybrid filtering: This ...

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Building a Recommendation System with R

by Michele Usuelli

Chapter 3. Recommender Systems

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