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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web
Key Features
- Build industry-standard recommender systems
- Only familiarity with Python is required
- No need to wade through complicated machine learning theory to use this book
Book Description
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.
What you will learn
- Get to grips with the different kinds of recommender systems
- Master data-wrangling techniques using the pandas library
- Building an IMDB Top 250 Clone
- Build a content based engine to recommend movies based on movie metadata
- Employ data-mining techniques used in building recommenders
- Build industry-standard collaborative filters using powerful algorithms
- Building Hybrid Recommenders that incorporate content based and collaborative fltering
Who this book is for
If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 147
Veröffentlichungsjahr: 2018
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Hands-On Recommendation Systems with Python
Copyright © 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor:Pravin DhandreAcquisition Editor: Noyonika DasContent Development Editor: Mohammed Yusuf ImaratwaleTechnical Editor: Jinesh TopiwalaCopy Editor: Safis EditingProject Coordinator: Hardik BhindeProofreader: Safis EditingIndexer: Priyanka DhadkeGraphics: Jason MonteiroProduction Coordinator: Nilesh Mohite
First published: July 2018
Production reference: 2310718
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78899-375-3
www.packtpub.com
To my mentor, Baran Toppare, and my friend, Utkarsh Bajpai, for their encouragement and support.
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Contributors
About the author
Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee. He has worked as a software engineer at Parceed, a New York start-up, and Springboard, an EdTech start-up based in San Francisco and Bangalore. He has also served as a backend development instructor at Acadview, teaching Python and Django to around 35 college students from Delhi and Dehradun.
He is an alumni of Springboard's data science career track. He has given talks at the SciPy India Conference and published popular tutorials on Kaggle and DataCamp.
About the reviewer
Dr. S. Gowrishankar is currently working as associate professor in the department of computer science and engineering at Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India. His current research interests are mainly focused on data science, including its technical aspects as well as its applications and implications. Specifically, he is interested in the applications of machine learning, data mining, and big data analytics in healthcare.
Image credits
Some of the images in the book are for illustrative purposes only. For more information refer to the links as follows:
Support Vector Machines Explained
, Tristan Fletcher (
https://static1.squarespace.com/static/58851af9ebbd1a30e98fb283/t/58902fbae4fcb5398aeb7505/1485844411772/SVM+Explained.pdf
How Are Principal Component Analysis And Singular Value Decomposition Related?
, Andre Perunicic (
https://intoli.com/blog/pca-and-svd/
Random Forest Classifier – Machine Learning
,
Balazs Holczer
(
http://www.globalsoftwaresupport.com/random-forest-classifier-bagging-machine-learning/
)
Machine Learning for Recommendation
, Dr. Rana Forsati (
http://www.cse.msu.edu/~forsati/
)
Approaches to analyse and interpret biological profile data
, Matthias Scholz (
http://phdthesis-bioinformatics-maxplanckinstitute-molecularplantphys.matthias-scholz.de/
)
K Means
, Image Courtesy of Michael Jordan (
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html
)
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Table of Contents
Title Page
Copyright and Credits
Hands-On Recommendation Systems with Python
Dedication
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Image credits
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Code in action
Conventions used
Get in touch
Reviews
Getting Started with Recommender Systems
Technical requirements
What is a recommender system?
The prediction problem
The ranking problem
Types of recommender systems
Collaborative filtering
User-based filtering
Item-based filtering
Shortcomings
Content-based systems
Knowledge-based recommenders
Hybrid recommenders
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 metric
The prerequisties
Calculating the score
Sorting and output
The knowledge-based recommender
Genres
The build_chart function
Summary
Building Content-Based Recommenders
Technical requirements
Exporting the clean DataFrame
Document vectors
CountVectorizer
TF-IDFVectorizer
The cosine similarity score
Plot description-based recommender
Preparing the data
Creating the TF-IDF matrix
Computing the cosine similarity score
Building the recommender function
Metadata-based recommender
Preparing the data
The keywords and credits datasets
Wrangling keywords, cast, and crew
Creating the metadata soup
Generating the recommendations
Suggestions for improvements
Summary
Getting Started with Data Mining Techniques
Problem statement
Similarity measures
Euclidean distance
Pearson correlation
Cosine similarity 
Clustering
k-means clustering
Choosing k
Other clustering algorithms
Dimensionality reduction
Principal component analysis
Other dimensionality reduction techniques
Linear-discriminant analysis
Singular value decomposition
Supervised learning
k-nearest neighbors
Classification
Regression
Support vector machines
Decision trees
Ensembling
Bagging and random forests
Boosting
Evaluation metrics
Accuracy
Root mean square error
Binary classification metrics
Precision
Recall
F1 score
Summary
Building Collaborative Filters
Technical requirements
The framework
The MovieLens dataset
Downloading the dataset
Exploring the data
Training and test data
Evaluation
User-based collaborative filtering
Mean
Weighted mean
User demographics
Item-based collaborative filtering
Model-based approaches
Clustering
Supervised learning and dimensionality reduction
Singular-value decomposition
Summary
Hybrid Recommenders
Technical requirements
Introduction
Case study – Building a hybrid model
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Recommendation systems are at the heart of almost every internet business today, from Facebook to Netflix 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 and buy from your platform.
This book shows you how to do just that. You will learn about 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 linear algebra and 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, collaborative filters that 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 familiarity with Python, and by the time you're finished, you will have a great grasp of how recommenders work, and you will be in a strong position to apply the techniques learned to your own problem domains.
Who this book is for
If you are a Python developer and want to develop applications for social networking, news personalization, or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but it's not mandatory.
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.
To get the most out of this book
This book will give you maximum benefit if you have some experience with Python development, or simply someone who wants to develop applications for social networking, news personalization, or smart advertising, this is the book for you. Having some knowledge of machine learning (ML) techniques will be helpful, but it is not mandatory.
Download the example code files
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Download the color images
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Code in action
Visit the following link to check out videos of the code being run:
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Getting Started with Recommender Systems
Almost everything we buy or consume today is influenced by some form of recommendation; whether that's from friends, family, external reviews, and, more recently, from the sources selling you the product. When you log on to Netflix or Amazon Prime, for example, you will see a list of movies and television shows the service thinks you will like based on your past watching (and rating) history. Facebook suggests people it thinks you may know and would probably like to add. It also curates a News Feed for you based on the posts you've liked, the people you've be-friended, and the pages you've followed. Amazon recommends items to you as you browse for a particular product. It shows you similar products from a competing source and suggests auxiliary items frequently bought together with the product.
So, it goes without saying that providing a good recommendation is at the core of successful business for these companies. It is in Netflix's best interests to engage you with content that you love so that you continue to subscribe to its service; the more relevant the items Amazon shows you, the greater your chances – and volume – of purchases will be, which directly translates to greater profits. Equally, establishing friendship is key to Facebook's power and influence as an almost omnipotent social network, which it then uses to churn money out of advertising.
In this introductory chapter, we will acquaint ourselves with the world of recommender systems, covering the following topics:
What is a recommender system? What can it do and not do?
The different types of recommender systems
