In Chapter 1, Getting Started with Recommender Systems, you will get a general introduction to recommender systems, such as collaborative filtering recommender systems, content-based recommender systems, knowledge-based recommender systems, and hybrid systems; it will also include a brief definition, real-world examples, and brief details of what one will be learning while building a recommender system.

In Chapter 2, Data Mining Techniques Used in Recommender Systems, gives you an overview of different machine learning concepts that are commonly used in building a recommender system and how a data analysis problem can be solved. This chapter includes data preprocessing techniques, such as similarity measures, dimensionality reduction, data mining techniques, and its evaluation techniques. Here similarity measures such as Euclidean distance, Cosine distance, Pearson correlation are explained. We will also cover data mining algorithms such as k-means clustering, support vector machines, decision trees, bagging, boosting, and random forests, along with a popular dimensional reduction technique, PCA. Evaluation techniques such as cross validation, regularization, confusion matrix, and model comparison are explained in brief.

In Chapter 3, Recommender Systems, we will discuss collaborative filtering recommender systems, an example for user- and item-based recommender systems, using the recommenderlab R package, and the MovieLens dataset. We will cover model building, which includes exploring data, splitting it into train and test datasets, and dealing with binary ratings. You will have an overview of content-based recommender systems, knowledge-based recommender systems, and hybrid systems.

In Chapter 4, Evaluating the Recommender Systems, we will learn about the evaluation techniques for recommender systems, such as setting up the evaluation, evaluating recommender systems, and optimizing the parameters.

In Chapter 5, Case Study – Building Your Own Recommendation Engine, we will understand a use case in R, which includes steps such as preparing the data, defining the rating matrix, building a recommender, and evaluating and optimizing a recommender.