Data preprocessing is a crucial step for any data analysis problem. The model's accuracy depends mostly on the quality of 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**), which are widely used in recommender systems. Apart from PCA, we have **singular value decomposition** (**SVD**), subset feature selection methods to reduce the dimensions of the dataset, but we limit our study to PCA.

#### Building a Recommendation System with R

#### Building a Recommendation System with R

#### Overview of this book

Table of Contents (13 chapters)

Building a Recommendation System with R

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

Free Chapter

Getting Started with Recommender Systems

Data Mining Techniques Used in Recommender Systems

Recommender Systems

Evaluating the Recommender Systems

Case Study – Building Your Own Recommendation Engine

References

Index

Customer Reviews