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)

The cosine similarity score

We will discuss similarity scores in detail in Chapter 5, Getting Started with Data Mining Techniques. Presently, we will make use of the cosine similarity metric to build our models. The cosine score is extremely robust and easy to calculate (especially when used in conjunction with TF-IDFVectorizer).

The cosine similarity score between two documents, x and y, is as follows:

The cosine score can take any value between -1 and 1. The higher the cosine score, the more similar the documents are to each other. We now have a good theoretical base to proceed to build the content-based recommenders using Python.