# Building a Recommendation System with R

Language: English

Pages: 135

ISBN: 1783554495

Format: PDF / Kindle (mobi) / ePub

**Learn the art of building robust and powerful recommendation engines using R**

## About This Book

- Learn to exploit various data mining techniques
- Understand some of the most popular recommendation techniques
- This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines

## Who This Book Is For

If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.

## What You Will Learn

- Get to grips with the most important branches of recommendation
- Understand various data processing and data mining techniques
- Evaluate and optimize the recommendation algorithms
- Prepare and structure the data before building models
- Discover different recommender systems along with their implementation in R
- Explore various evaluation techniques used in recommender systems
- Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems

## In Detail

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.

## Style and approach

This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

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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 areaCase (C): This is the case for each user, containing its IDVote (V): This is the vote lines for the case Each case record is

* (1 - weight_description) recc_model@model$sim <- as(dist_tot, "dgCMatrix") Predict the test set users with known purchases. Since we are using a table with 0 and 1 ratings only, we can specify that we predict the top n recommendations with the argument type = "topNList". The argument n, defining the number of items to recommend, comes from the items_to_recommend input: eval_prediction <- predict(object = recc_model, newdata = getData(eval_sets, "known"), n = items_to_recommend, type =

performance metrics. In order to evaluate our model, we can use the precision and recall. See Chapter 4, Evaluating the Recommender Systems for more information. We can extract a vector of precisions (or recalls) using sapply: sapply(list_performance, "[[", "precision")^t 0.1663, 0.1769, 0.1769, 0.175, 0.174, 0.1808, 0.176, 0.1779, 0.1788, 0.1788, 0.1808, 0.1817, 0.1817, 0.1837, 0.1846, 0.1837, 0.1827, 0.1817, 0.1827, 0.1827, 0.1817, 0.1808, 0.1817, 0.1808, 0.1808, 0.1827, 0.1827, 0.1837,

the data. In general, any data preprocessing step involves data cleansing, transformations, identifying missing values, and how they should be treated. Only the preprocessed data can be fed into a machine-learning algorithm. In this section, we will focus mainly on data preprocessing techniques. These techniques include similarity measurements (such as Euclidean distance, Cosine distance, and Pearson coefficient) and dimensionality-reduction techniques, such as Principal component analysis (PCA),

package. Pearson correlation Similarity between two products can also be given by the correlation existing between their variables. Pearson's correlation coefficient is a popular correlation coefficient calculated between two variables as the covariance of the two variables divided by the product of their standard deviations. This is given by ƿ (rho): R script is given by these lines of code: Coef = cor(mtcars, method="pearson") where mtcars is the dataset Empirical studies showed that