Book Image

Hands-On Recommendation Systems with Python

By : Rounak Banik
Book Image

Hands-On Recommendation Systems with Python

By: Rounak Banik

Overview of this book

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques  With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
Table of Contents (9 chapters)

Summary

In this chapter, we built a simple recommender, which was a clone of the IMDB Top 250 chart. We then proceeded to build an improved knowledge-based recommender, which asked the user for their preferred genres, duration, and time. In the process of building these models, we also learned to perform some advanced data wrangling with the Pandas library.

In the next chapter, we will use more advanced features and techniques to build a content-based recommender. This model will be able to detect similar movies based on their plots and recommend movies by identifying similarities in genre, cast, crew, plot, and so on.