Banik - Hands-On Recommendation Systems with Python: Start Building Powerful and Personalized, Recommendation Engines with Python

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First published: July 2018

Production reference: 2310718

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ISBN 978-1-78899-375-3

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To my mentor, Baran Toppare, and my friend, Utkarsh Bajpai, for their encouragement and support.

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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.

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.

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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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.