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

Read now

Unlock full access

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

Summary

This chapter showed you how to evaluate the performance of different models in order to choose the most accurate one. There are different ways to evaluate performances that might potentially lead to different choices. Depending on the business target, the evaluation metric is different. This is an example of how business and data should be combined to achieve the final result.

The next chapter will explain a complete use case in which we will prepare the data, build different models, and test them.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Practical Machine Learning: Innovations in Recommendation

Publisher Resources

ISBN: 9781783554492

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Building a Recommendation System with R

by Michele Usuelli

Summary

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.