August 20-23, 2019 | MONTREAL
About
Summary
Recommender systems find patterns in user behaviour to improve personalized experiences and understand the environment that they are acting in. They are ubiquitous and are most often used to recommend items to users (for example, books and movies on Amazon and Netflix, relevant documentation in large software projects, or papers of interest to scientists).
The workshop on recommender systems will be held in English. This workshop includes theoretical sessions in the morning and hands-on sessions in the afternoon.
Generic objectives
- Train industry professionals and students in the fields of data science, machine learning and operational research;
- Develop a better understanding of the challenges and problems in data science applied to a specific field;
- Learn to use software tools to solve practical industry-related problems;
- Offer a networking opportunity between students and professionals from industry;
- Facilitate knowledge transfer between academia and industry.
Prerequisites
Basic knowledge of mathematics and programming (ideally Python) is strongly recommended.
Date and place
August 20-23, 2019
HEC Montréal – Pavillon CSC
3000, chemin de la côte Sainte-Catherine, salle Procter et Gamble
Montréal, QC H3T 2A7
Canada
Registration
Organizers
- Laurent Charlin, HEC Montréal (and Mila)
- Brian Moore, IVADO
Contact
For additional questions or inquiries, please contact formations@ivado.ca.
To have access to the presentations:
https://drive.google.com/drive/folders/1biECfOYhOmWgHSx_DVgSgVNNpEiM_c4Q?usp=sharing
Confirmed speakers
David Berger
David Berger is a PhD student in computer science at the Université de Montréal…
Laurent Charlin
Laurent Charlin is an assistant professor of artificial intelligence at HEC Montréal and a member of Mila–Quebec Artificial Intelligence Institute.
Didier Chételat
Didier Chételat is a researcher at the Canada Excellence Research Chair in Data Science for Real-Time Decision Making…
Fernando Diaz
Principal Researcher and lead of the Montreal FATE Research Group at Microsoft
Michael Ekstrand
Michael Ekstrand is an assistant professor in the Department of Computer Science at Boise State University.
Jonathan Guymont
Jonathan Guymont has a B.Sc degree in mathematics from Université de Montreal.
Dawen Liang
Dawen Liang is a senior research scientist at Netflix, working on core personalization algorithms
James McInerney
James McInerney is a senior research scientist at Netflix Research focusing on causality and Bayesian inference for recommendation.
Bhaskar Mitra
Bhaskar Mitra is a Principal Applied Scientist at Bing in the Microsoft Research Montreal lab
Program
SCHEDULE:
09:00-12:00: Presentation
Introduction to machine learning and deep learning
12:00-13:00: Lunch (included)
13:00-16:00: Hands-on tutorial
Machine learning in action (Bring your own laptop!)
David Berger (Université de Montréal) & Didier Chételat (Polytechnique Montréal)
There will be a coffee break in the morning and afternoon.
SCHEDULE:
09:00-12:00: Presentation
Recommender systems basics and deep learning for recommender systems
Laurent Charlin (HEC/Mila)
12:00-13:00: Lunch (included)
13:00-14:30: Presentation
Learning to rank
Bhaskar Mitra (Microsoft Research)
15:00-17:00: Hands-on tutorial
Recommender systems in action (Bring your own laptop!)
There will be a coffee break in the morning and afternoon.
SCHEDULE:
09:00-12:00: Presentation
Contextual Bandits in recommender systems
James McInerney (Netflix)
12:00-13:00: Lunch (included)
13:00-16:00: Presentation
Fairness in recommender systems
Michael Ekstrand (Boise State)
There will be a coffee break in the morning and afternoon.
SCHEDULE:
09:00-12:00: Presentation
Advanced modelling
Dawen Liang (Netflix)
12:00-13:00: Lunch (included)
13:00-16:00: Presentation
Evaluating recommender systems
There will be a coffee break in the morning and afternoon.