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  • Book Overview & Buying Building a Recommendation System with R
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Building a Recommendation System with R

Building a Recommendation System with R

By : Usuelli
4.5 (8)
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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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Citation
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Recommender Systems
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1. Getting Started with Recommender Systems
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Understanding recommender systems
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The structure of the book
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Collaborative filtering recommender systems
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Content-based recommender systems
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Knowledge-based recommender systems
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Hybrid systems
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Evaluation techniques
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A case study
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The future scope
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Summary
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2. Data Mining Techniques Used in Recommender Systems
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2. Data Mining Techniques Used in Recommender Systems
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Solving a data analysis problem
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Data preprocessing techniques
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Data mining techniques
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Cluster analysis
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Decision trees
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Ensemble methods
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Evaluating data-mining algorithms
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Summary
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3. Recommender Systems
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3. Recommender Systems
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R package for recommendation – recommenderlab
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Data exploration
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Data preparation
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Item-based collaborative filtering
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User-based collaborative filtering
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User-based collaborative filtering on binary data
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Content-based filtering
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Hybrid recommender systems
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Knowledge-based recommender systems
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Summary
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4. Evaluating the Recommender Systems
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4. Evaluating the Recommender Systems
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Preparing the data to evaluate the models
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Evaluating recommender techniques
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Identifying the most suitable model
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Summary
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5. Case Study – Building Your Own Recommendation Engine
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5. Case Study – Building Your Own Recommendation Engine
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Preparing the data
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Building the model
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Evaluating and optimizing the model
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Summary
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A. References
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A. References
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Index
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Index

User-based collaborative filtering

In the previous section, the algorithm was based on items and the steps to identify recommendations were as follows:

  • Identify which items are similar in terms of having been purchased by the same people

  • Recommend to a new user the items that are similar to its purchases

In this section, we will use the opposite approach. First, given a new user, we will identify its similar users. Then, we will recommend the top-rated items purchased by similar users. This approach is called user-based collaborative filtering. For each new user, these are the steps:

  1. Measure how similar each user is to the new one. Like IBCF, popular similarity measures are correlation and cosine.

  2. Identify the most similar users. The options are:

    • Take account of the top k users (k-nearest_neighbors)

    • Take account of the users whose similarity is above a defined threshold

  3. Rate the items purchased by the most similar users. The rating is the average rating among similar users and the approaches are...

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