Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Building a Recommendation System with R
  • Table Of Contents Toc
Building a Recommendation System with R

Building a Recommendation System with R

By : Usuelli
4.5 (8)
Buy this Book
close
close
Building a Recommendation System with R

Building a Recommendation System with R

4.5 (8)
By: Usuelli
Buy this Book

Overview of this book

A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system.
Table of Contents (8 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Citation
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Getting Started with Recommender Systems
Icon
1. Getting Started with Recommender Systems
Icon
Understanding recommender systems
Icon
The structure of the book
Icon
Collaborative filtering recommender systems
Icon
Content-based recommender systems
Icon
Knowledge-based recommender systems
Icon
Hybrid systems
Icon
Evaluation techniques
Icon
A case study
Icon
The future scope
Icon
Summary
2
2. Data Mining Techniques Used in Recommender Systems
chevron up
Icon
2. Data Mining Techniques Used in Recommender Systems
Icon
Solving a data analysis problem
Icon
Data preprocessing techniques
Icon
Data mining techniques
Icon
Cluster analysis
Icon
Decision trees
Icon
Ensemble methods
Icon
Evaluating data-mining algorithms
Icon
Summary
3
3. Recommender Systems
Icon
3. Recommender Systems
Icon
R package for recommendation – recommenderlab
Icon
Data exploration
Icon
Data preparation
Icon
Item-based collaborative filtering
Icon
User-based collaborative filtering
Icon
User-based collaborative filtering on binary data
Icon
Content-based filtering
Icon
Hybrid recommender systems
Icon
Knowledge-based recommender systems
Icon
Summary
4
4. Evaluating the Recommender Systems
Icon
4. Evaluating the Recommender Systems
Icon
Preparing the data to evaluate the models
Icon
Evaluating recommender techniques
Icon
Identifying the most suitable model
Icon
Summary
5
5. Case Study – Building Your Own Recommendation Engine
Icon
5. Case Study – Building Your Own Recommendation Engine
Icon
Preparing the data
Icon
Building the model
Icon
Evaluating and optimizing the model
Icon
Summary
6
A. References
Icon
A. References
7
Index
Icon
Index

Cluster analysis

Cluster analysis is the process of grouping objects together in a way that objects in one group are more similar than objects in other groups.

An example would be identifying and grouping clients with similar booking activities on a travel portal, as shown in the following figure.

In the preceding example, each group is called a cluster, and each member (data point) of the cluster behaves in a manner similar to its group members.

Cluster analysis

Cluster analysis

Cluster analysis is an unsupervised learning method. In supervised methods, such as regression analysis, we have input variables and response variables. We fit a statistical model to the input variables to predict the response variable. Whereas in unsupervised learning methods, however, we do not have any response variable to predict; we only have input variables. Instead of fitting a model to the input variables to predict the response variable, we just try to find patterns within the dataset. There are three popular clustering algorithms...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Building a Recommendation System with R
End of Section 2
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon