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Hands-On Recommendation Systems with Python

By : Banik

3.4 (11)

Buy this Book

Hands-On Recommendation Systems with Python

3.4 (11)

By: Banik

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Getting Started with Recommender Systems

Technical requirements

What is a recommender system?

Types of recommender systems

Summary

Manipulating Data with the Pandas Library

Technical requirements

Setting up the environment

The Pandas library

The Pandas DataFrame

The Pandas Series

Summary

Building an IMDB Top 250 Clone with Pandas

Technical requirements

The simple recommender

The knowledge-based recommender

Summary

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

Getting Started with Data Mining Techniques

Problem statement

Similarity measures

Clustering

Dimensionality reduction

Supervised learning

Evaluation metrics

Summary

Building Collaborative Filters

Technical requirements

The framework

User-based collaborative filtering

Item-based collaborative filtering

Model-based approaches

Summary

Hybrid Recommenders

Technical requirements

Introduction

Case study – Building a hybrid model

Summary

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Building Content-Based Recommenders

In the previous chapter, we built an IMDB Top 250 clone (a type of simple recommender) and a knowledge-based recommender that suggested movies based on timeline, genre, and duration. However, these systems were extremely primitive. The simple recommender did not take into consideration an individual user's preferences. The knowledge-based recommender did take account of the user's preference for genres, timelines, and duration, but the model and its recommendations still remained very generic.

Imagine that Alice likes the movies The Dark Knight, Iron Man, and Man of Steel. It is pretty evident that Alice has a taste for superhero movies. However, our models from the previous chapter would not be able to capture this detail. The best it could do is suggest action movies (by making Alice input action as the preferred genre), which is...