Recommendation Systems

“Sure. I do marathons… on Netflix.”

In the digital world, recommendation systems play a significant role - both for the users and for the company. For the users, a new world of options are thrown up - that were hitherto tough to find. For companies, it helps drive up user engagement and satisfaction, directly impacting their bottom line. If you’ve shopped on an e-commerce site or watched a movie on an on-demand video platform you would’ve seen options like: “People who viewed this product also viewed…” “Products similar to this one…”. These are the results from recommendation systems.

In this workshop, you will learn the different paradigms of recommendation systems and get introduced to the usage of machine-learning and deep-learning based approaches. By the end of the workshop, you will have enough practical hands-on knowledge to build, select, deploy and maintain a recommendation system for your problem.

Workshop Objectives

The aim of the workshop is to provide a thorough introduction to the art and science of building recommendation systems. These are the main objectives:

Key Concepts

Workshop Design

This would be a two-day instructor-led hands-on workshop to learn and implement an end-to-end deep learning model for recommendation systems. This is predominantly a hands-on course and will be 70% programming/coding and 30% theory. It would aim to cover the following topics.

Session #1: Introduction

Session #2: Content-Based

Session #3: Colloborative-Filtering

Session #4: Learning-to-Rank

Session #5: Hybrid Recommender

Session #6: Time and Context

Session #7: Deployment & Monitoring

Session #8: Evaluation, Challenges & Way Forward

Workshop Details

Participant Profile — A programmer interested in adding data-driven recommendations to their products or a beginner in data scientist with experience in using machine learning & interested to build a deeper and more applied perspective in using ML & DL for recommndation systems.

Pre-requisite skills — This is a hands-on course and hence, participants should be comfortable with programming in python and have exposure to python data stack.

Tools Used — The workshop principles are tool-agnostic and can be applied using any data stack and platform for building recommendation systems. However, for the ease of doing the exercises, we would be using Python Data Stack during the workshop. All notebooks and required data-sets will be provided using a cloud hosted environment. No additional downloads required. Participants will only require to have a browser with internet connectivity on their own laptop.

Number of Participants — The maximum number of participants for the workshop would be capped at 30. The small class size would enable a more participative environment with group interaction possible as well as opportunities to have one-to-one learning interactions.

Duration — The workshop would be conducted over 2 days from 0930 to 1730. There will be short breaks during the morning and afternoon session and a longer lunch break of around 45 minutes in the middle.

Facilitators’ Profile

Amit Kapoor teaches the craft of telling visual stories with data. He conducts workshops and trainings on Data Science in Python and R, as well as on Data Visualisation topics. His background is in strategy consulting having worked with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps.

Bargava Subramanian is a practicing Data Scientist. He has 14 years of experience delivering business analytics solutions to Investment Banks, Entertainment Studios and High-Tech companies. He has given talks and conducted workshops on Data Science, Machine Learning, Deep Learning and Optimization in Python and R. He has a Masters in Statistics from University of Maryland, College Park, USA. He is an ardent NBA fan. You can tweet to him at @bargava.