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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 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 combines different techniques.

  • Knowledge-based filtering: This is uses explicit knowledge about users and items.

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