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  • Recommender Systems: Behind the Screen

Recommender Systems: Behind the Screen

Ref. RECM1EN
CategoryedXCategoryArtificial and numeric intelligenceCategoryPaid Certificate
  • Duration: 6 weeks
  • Effort: 30 hours
  • Pace: ~5 hours/week

This course is given on the edX platform.

How are items recommended when you’re browsing for movies, jobs or clothing online? Register here and you’ll discover the fundamental concepts and methods allowing the most relevant item suggestions to users from e-commerce to online advertisement.

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RECM1EN+2T2025

Enrollment
From May 1, 2025 to April 30, 2026
Course
From May 1, 2025 to April 30, 2026
Languages
English
Enroll now

Description

In this course, you will explore and learn the best methods and practices in recommender systems, which are an essential component of the online ecosystem. This course was developed by IVADO and HEC Montréal, as part of a workshop that took place in Montreal. You will be accompanied throughout and given concrete examples by seven international experts from both Academia and Industry.

Recommender systems are algorithms that find patterns in user behaviour to improve personalized experiences and understand their environment. They are ubiquitous and are most often used to recommend items to users, for example, books, movies, but also possible friends, food recipes or even relevant documentation in large software projects, or papers of interest to scientists.

The content of this MOOC is an introduction to the field of recommender systems. The outline includes: machine learning for recommender systems followed by an introduction to evaluation methods; advanced modelling; contextual bandits; ranking methods; and fairness and discrimination in recommender systems.

The course is primarily intended for industry professionals and academics with basic (first-year undergraduate) knowledge in mathematics and programming (ideally Python). Graduate students in science and engineering (mainly those who are not yet familiar with machine learning and recommender systems) may find this content instructive and compelling. The content of this course will also be of great use to whomever uses or is interested in AI, in any other way.

We estimate that it takes 6 weeks to follow this class. The course is divided into relevant segments that you may watch at your own pace. There are comprehensive quizzes at the end of each segment to evaluate your understanding of the content. You will also practice recommender systems algorithms thanks to a tutorial guided by an expert. Also, a second self-practice module will be offered to participants who will register for the course with the Verified Certificate.

We welcome you to this special learning journey of Recommender Systems: Behind the Screen!

This course is brought to you by IVADO, HEC Montréal and Université de Montréal.

IVADO is a Québec-wide collaborative institute in the field of digital intelligence.

HEC Montréal is a French-language university offering internationally renowned management education and research.

Université de Montréal is one of the world’s leading research universities.

What you'll learn

At the end of the MOOC, participants should be able to:

  • Understand the basics of recommender systems including its terminology;
  • Identify the types of problems and the recommender systems’ methods to solve those;
  • Apply the methodology for carrying out a project in recommender systems;
  • Use recommender systems’ algorithms through practical and tutorial sessions.

Syllabus

MODULE 1 Machine Learning for Recommender Systems

  • Score Models
  • Practical Aspects

TUTORIAL MODULE Matrix Factorization

MODULE 2 Evaluations for Recommender Systems

  • Offline (Batch) Evaluation
  • Online (Production) Evaluation

MODULE 3 Advanced modelling

  • Extending Basic Models
  • A missing Data Perspective

SELF-PRACTICE MODULE

Autoencoders (this module is assessed and offered only to participants who register for the course with the Verified Certificate)

MODULE 4 Contextual Bandits

  • Introduction to Bandits
  • Putting it All Together

MODULE 5 Learning to Rank

  • Learning to Rank with Neural Networks
  • Learning to Rank with Deep Neural Networks

MODULE 6 Fairness and Discrimination in Recommender Systems

  • Algorithmic Fairness
  • Fairness in Information Retrieval

Certificate

Pursue a Verified Certificate to highlight the knowledge and skills you gain for Can$150.

Other course runs

Archived

  • RECM1ENx-P2021, enrollment from Jan. 21, 2021 to Feb. 27, 2022
  • RECM1ENx, enrollment from Sept. 26, 2023 to June 13, 2024
  • RECM1EN+2T2024, enrollment from July 15, 2024 to April 30, 2025

Course team

Charlin, Laurent

Categories

Ph.D., Assistant Professor, Department of Decision Sciences, HEC Montreal

Diaz, Fernando

Categories

Ph.D., Research Scientist at Google
Member of Mila – Quebec Artificial Intelligence Institute

Ekstrand, Michael

Categories

Ph.D., Assistant Professor, Department of Computer Science, Boise State University

Jambor, Dora

Categories

Graduate student, Computer Science, McGill University, 
Researcher at Mila, the Quebec Artificial Intelligence Institute

Liang, Dawen

Categories

Ph.D., Senior Research Scientist at Netflix

McInerney, James

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Ph.D., Senior Research Scientist at Netflix

Mitra, Bhaskar

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Ph.D., Principal Applied Scientist at Microsoft

Scientific assistants

Inés MORENO BOLUDA, BSc.
Audio Visual Technician, IVADO
Student, McGill University 

Amir RAZA, M.Sc
Scientific Assistant, IVADO
Research Masters Student, MILA , Université de Montréal

Scientific and project coordination

Nathalie SANON, Ph.D.
Head of IVADO Training Program  

Nabila OUCHENE, M.Sc.
Coordinator of IVADO Online Training Program

Robert GÉRIN-LAJOIE, M. Sc. Ift
Special Project Advisor
Centre de Pédagogie Universitaire, Université de Montréal

Instructuonal design

Tetyana TSOMKO
Instructional Designer
Centre de Pédagogie Universitaire, Université de Montréal

Media

Cédric JOYAL
Media Designer, Centre de Pédagogie Universitaire, Université de Montréal

Support & quality control

Vincent LABERGE,
Technopedagogical Support & Quality Control Assessor, EDUlib
Centre de Pédagogie Universitaire, Université de Montréal 

Organizations

Université de Montréal

IVADO

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