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Gorakala - Building recommendation engines: understand your data and user preferences to make intelligent, accurate, and profitable decisions

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Gorakala Building recommendation engines: understand your data and user preferences to make intelligent, accurate, and profitable decisions
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Building Recommendation Engines

Building Recommendation Engines

Copyright 2016 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 authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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.

First published: December 2016

Production reference: 1231216

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-78588-485-6

www.packtpub.com

Credits

Author

Suresh Kumar Gorakala

Copy Editor

Manisha Sinha

Reviewers

Vikram Dhillon

Vimal Romeo

Project Coordinator

Nidhi Joshi

Commissioning Editor

Veena Pagare

Proofreader

Safis Editing

Acquisition Editor

Tushar Gupta

Indexer

Mariammal Chettiyar

Content Development Editor

Manthan Raja

Graphics

Disha Haria

Technical Editor

Dinesh Chaudhary

Production Coordinator

Arvindkumar Gupta

About the Author

Suresh Kumar Gorakala is a Data scientist focused on Artificial Intelligence. He has professional experience close to 10 years, having worked with various global clients across multiple domains and helped them in solving their business problems using Advanced Big Data Analytics. He has extensively worked on Recommendation Engines, Natural language Processing, Advanced Machine Learning, Graph Databases.He previously co-authored Building a Recommendation System with R for Packt Publishing.He is passionate traveler and is photographer by hobby.

I would like to thank my wife for putting up with my late-night writing sessions and all my family members for supporting me over the months. I also give deep thanks and gratitude to Barathi Ganesh, Raj Deepthi, Harsh and my colleagues who without their support this book quite possibly would not have happened.I would also like to thank all the mentors that Ive had over the years. Without learning from these teachers, there is not a chance I could be doing what I do today, and it is because of them and others that I may not have listed here that I feel compelled to pass my knowledge on to those willing to learn. I would also like to thank all the reviewers and project managers of the book to make it a reality.

About the Reviewers

Vikram Dhillon is a software developer, a bioinformatics researcher, and a software coach at the Blackstone LaunchPad in the University of Central Florida. He has been working on his own startup involving healthcare data security of late. He lives in Orlando and regularly attends development meetups and hackathons. He enjoys spending his spare time reading about new technologies, such as the Blockchain and developing tutorials for machine learning in game design. He has been involved in open-source projects for over five years and writes about technology and startups at opsbug.com

Vimal Romeo is a data science at Ernst and Young, Rome. He holds a masters degree in Big Data Analytics from Luiss Business School, Rome. He also holds an MBA degree from XIME ,India and a bachelors degree in computer science and engineering from CUSAT, India. He is an author at MilanoR which is a blog related to the R language.

I would like to thank my mom Mrs Bernadit and my brother - Vibin for their continuous support. I would also like to thank my friends Matteo Amadei, Antonella Di Luca, Asish Mathew and Eleonora Polidoro who supported me during this process. A special thanks to Nidhi Joshi from Packt Publishing for keeping me motivated during the process.

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Preface

Building Recommendation Engines is a comprehensive guide for implementing Recommendation Engines such as collaborative filtering, content based recommendation engines, context aware recommendation engines using R, Python, Spark, Mahout, Neo4j technologies. The book covers various recommendation engines widely used across industries with their implementations. This book also covers a chapter on popular datamining techniques commonly used in building recommendations and also discuss in brief about the future of recommendation engines at the end of the book.

What this book covers

, Introduction to Recommendation Engines , will be a refresher to Data Scientists and an introduction to the beginners of recommendation engines. This chapter introduces popular recommendation engines that people use in their day-to-day lives. Popular recommendation engine approaches available along with their pros and cons are covered.

, Build Your First Recommendation Engine , is a short chapter about how to build a movie recommendation engine to give a head start for us before we take off into the world of recommendation engines.

, Recommendation Engines Explained , is about different recommendation engine techniques popularly employed, such as user-based collaborative filtering recommendation engines, item-based collaborative filtering, content-based recommendation engines, context-aware recommenders, hybrid recommenders, model-based recommender systems using Machine Learning models and mathematical models.

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