Building Recommendation Engines
Copyright 2016 Packt Publishing
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First published: December 2016
Production reference: 1231216
Published by Packt Publishing Ltd.
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B3 2PB, UK.
ISBN 978-1-78588-485-6
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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.