What this book covers

Chapter 1, Getting Started with Recommender Systems, introduces the recommendation problem and the models popularly used to solve it.

Chapter 2, Manipulating Data with the Pandas Library, illustrates various data wrangling techniques using the Pandas library.

Chapter 3, Building an IMDB Top 250 Clone with Pandas, walks through the process of building a top movies chart and a knowledge-based recommender that explicitly takes in user preferences.

Chapter 4, Building Content-Based Recommenders, describes the process of building models that make use of movie plot lines and other metadata to offer recommendations.

Chapter 5, Getting Started with Data Mining Techniques, covers various similarity scores, machine learning techniques, and evaluation metrics used to build and gauge performances of collaborative recommender models.

Chapter 6, Building Collaborative Filters, walks through the building of various collaborative filters that leverage user rating data to offer recommendations.

Chapter 7, Hybrid Recommenders, outlines various kinds of hybrid recommenders used in practice and walks you through the process of building a model that incorporates both content and collaborative-based filtering.