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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

Preparing the data

Starting from raw data, this section will show you how to prepare the input for the recommendation models.

Description of the data

The data is about Microsoft users visiting a website during one week. For each user, the data displays which areas the users visited. For the sake of simplicity, from now on we will refer to the website areas with the term "items".

There are 5,000 users and they are represented by sequential numbers between 10,001 and 15,000. Items are represented by numbers between 1,000 and 1,297, even if they are less than 298.

The dataset is an unstructured text file. Each record contains a number of fields between 2 and 6. The first field is a letter defining what the record contains. There are three main types of records, which are as follows:

  • Attribute (A): This is the description of the website area

  • Case (C): This is the case for each user, containing its ID

  • Vote (V): This is the vote lines for the case

Each case record is followed by one or more votes, and...

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