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.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Recommender Systems
Getting Started with Recommender Systems
Technical requirements
What is a recommender system?
Types of recommender systems
Summary
2
Manipulating Data with the Pandas Library
Manipulating Data with the Pandas Library
Technical requirements
Setting up the environment
The Pandas library
The Pandas DataFrame
The Pandas Series
Summary
3
Building an IMDB Top 250 Clone with Pandas
Building an IMDB Top 250 Clone with Pandas
Technical requirements
The simple recommender
The knowledge-based recommender
Summary
4
Building Content-Based Recommenders
Building Content-Based Recommenders
Technical requirements
Exporting the clean DataFrame
Document vectors
The cosine similarity score
Plot description-based recommender
Metadata-based recommender
Suggestions for improvements
Summary
5
Getting Started with Data Mining Techniques
Getting Started with Data Mining Techniques
Problem statement
Similarity measures
Clustering
Dimensionality reduction
Supervised learning
Evaluation metrics
Summary
6
Building Collaborative Filters
Building Collaborative Filters
Technical requirements
The framework
User-based collaborative filtering
Item-based collaborative filtering
Model-based approaches
Summary
7
Hybrid Recommenders
Hybrid Recommenders
Technical requirements
Introduction
Case study – Building a hybrid model
Summary
8
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Building an IMDB Top 250 Clone with Pandas

The Internet Movie Database (IMDB) maintains a chart called the IMDB Top 250, which is a ranking of the top 250 movies according to a certain scoring metric. All the movies in this list are non-documentary, theatrical releases with a runtime of at least 45 minutes and over 250,000 ratings:

This chart can be considered the simplest of recommenders. It doesn't take into consideration the tastes of a particular user, nor does it try to deduce similarities between different movies. It simply calculates a score for every movie based on a predefined metric and outputs a sorted list of movies based on that score.

In this chapter, we will be covering the following:

  • Building a clone of the IMDB Top 250 chart (henceforth referred to as the simple recommender).
  • Taking the functionalities of the chart one step further and building a knowledge...
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