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Building Recommender Systems: Fundamentals and Practice
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Building Recommender Systems: Fundamentals and Practice

Learn to design and implement personalized recommendation algorithms for e-commerce and online platforms.

Course Cost

₹ 16,167

Beginner

Skill Level

6 Weeks

Self-paced lessons

This comprehensive course explores the fundamentals of recommender systems, developed by IVADO and HEC Montréal. Led by seven international experts, students learn essential algorithms and techniques for creating personalized recommendation systems. The curriculum covers machine learning applications, evaluation methods, advanced modeling, and ethical considerations in recommendation systems. Through practical tutorials and hands-on exercises in Python, participants gain real-world experience in implementing recommendation algorithms for various applications.

Laurent CHARLIN

Laurent CHARLIN

Fernando DIAZ

Fernando DIAZ

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Laurent CHARLIN

Laurent CHARLIN

Fernando DIAZ

Fernando DIAZ

What you'll learn

  • Master core concepts and terminology of recommender systems

  • Identify appropriate recommendation methods for specific problems

  • Implement recommendation algorithms using Python

  • Evaluate and optimize recommender system performance

  • Understand advanced modeling techniques and neural networks

  • Apply contextual bandits and learning-to-rank methods

Skills you'll gain

Recommender Systems
Machine Learning
Artificial Intelligence
Python Programming
Algorithm Design
Data Analysis
Neural Networks
Matrix Factorization

This course includes:

PreRecorded video

Graded assignments, exams

Access on Mobile, Tablet, Desktop

Limited Access access

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There are 6 modules in this course

This course provides a comprehensive introduction to recommender systems, essential components of modern online platforms. The curriculum covers fundamental concepts and advanced techniques in recommendation algorithms, including machine learning applications, evaluation methods, and ethical considerations. Students learn through a combination of theoretical instruction and practical implementation, with hands-on tutorials in Python. The course emphasizes both technical proficiency and understanding of real-world applications, preparing participants to develop effective recommendation systems for various platforms.

Machine Learning for Recommender Systems

Module 1

Evaluations for Recommender Systems

Module 2

Advanced modelling

Module 3

Contextual Bandits

Module 4

Learning to Rank

Module 5

Fairness and Discrimination in Recommender Systems

Module 6

Fee Structure

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Laurent CHARLIN
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3

Courses

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Laurent Charlin serves as Associate Professor at HEC Montréal and Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute. After completing his PhD from the University of Toronto, MMath from the University of Waterloo, and postdoctoral work at Princeton, Columbia, and McGill Universities, he has established himself as a leading researcher in machine learning and decision-making systems. His research focuses on developing novel machine learning models, particularly in recommender systems, dialogue systems, and optimization. His most significant contribution includes co-developing the Toronto Paper Matching System (TPMS), widely adopted by computer science conferences for reviewer assignment. His recent work explores continual learning, with applications in fields such as recommender systems and optimization. As a core academic member of Mila and professor at HEC Montréal's Department of Decision Sciences, he continues to advance the field through research in Bayesian statistics, deep learning, and artificial intelligence while contributing to AI literacy through MOOCs and public engagement.
Fernando DIAZ
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2

Courses

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Fernando Diaz serves as Research Scientist at Google and holds a CIFAR AI Chair at Mila. After completing his PhD from the University of Massachusetts Amherst and BS from the University of Michigan, he has established himself as a leading researcher in information retrieval and AI ethics. His career includes significant roles as Assistant Managing Director at Microsoft Research Montreal, where he led the FATE (Fairness, Accountability, Transparency and Ethics in AI) research group, and Director of Research at Spotify, where he helped establish their research organization. His work has earned numerous accolades, including the 2017 British Computer Society Karen Spärck Jones Award and special recognition at major conferences including SIGIR, CIKM, and WSDM. His research focuses on developing ethical AI systems, particularly in information access and recommendation systems, while addressing their societal implications. Through his leadership in organizing key conferences and workshops in AI ethics and information retrieval, he continues to shape the discourse on responsible AI development.
Michael EKSTRAND
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2

Courses

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Michael D. Ekstrand serves as Assistant Professor at Drexel University's College of Computing & Informatics, where he focuses on human-computer interaction and artificial intelligence research. After completing his PhD from the University of Minnesota, he has established himself as a leading researcher in recommender systems and algorithmic fairness. His work aims to ensure information access systems benefit the people they affect, leading to his receipt of the prestigious NSF CAREER award in 2018. As creator and leader of the LensKit open-source project and co-director of the People and Information Research Team (PIReT), he advances understanding of how recommender systems impact users and society. His research combines technical expertise with ethical considerations, focusing on developing fair and effective information systems while addressing issues of bias and discrimination in algorithmic systems.
Dora JAMBOR
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2

Courses

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Dora Jambor serves as a graduate student in Computer Science at McGill University and researcher at Mila - Quebec AI Institute, where she works under the supervision of Joelle Pineau and Dzmitry Bahdanau. Her research focuses on semantic parsing, specifically developing AI models that can efficiently translate natural language into executable programs with better generalization capabilities. Before her academic career, she worked as a machine learning engineer at Shopify, developing recommender systems for personalization and search problems. Her experience includes roles as Visiting Researcher at ServiceNow and Scientist in Residence at Creative Destruction Labs and NextAI, where she advised various startups. Her recent work includes significant contributions to graph representation learning, particularly through her paper on few-shot link prediction in knowledge graphs. Through her research at the intersection of natural language processing and graph representation learning, she continues to advance the field while developing more efficient and generalizable AI models.
Dawen LIANG
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1

Course

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Dawen Liang serves as Senior Research Scientist at Netflix, where he focuses on developing core personalization algorithms and recommendation systems. After completing his PhD from Columbia University's Electrical Engineering Department, he has established himself as a leading researcher in probabilistic modeling and machine learning. His work spans multiple domains including variational autoencoders for collaborative filtering, causal inference for recommendation systems, and deep learning applications. His significant contributions include pioneering work in music signal analysis through librosa, which has garnered over 3,000 citations, and developing innovative approaches to recommendation systems that have been implemented at Netflix. Through his research on probabilistic models, approximate inference, and causal inference, he has helped advance Netflix's personalization capabilities, particularly in addressing challenges of offline-online metric alignment and heterogeneous feature integration
James McINERNEY
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1

Course

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James McInerney serves as Senior Research Scientist at Netflix, where he focuses on statistical approaches to machine learning, particularly in epistemic uncertainty quantification, temporal point process models, and causal reasoning. After completing his PhD in machine learning for spatiotemporal modeling from the University of Southampton, MSc in Artificial Intelligence from Imperial College London, and BA in Computer Science from Oxford University, he has established himself as a leading researcher in Bayesian approaches to recommender systems. His career includes significant roles as Senior Research Scientist at Spotify and postdoctoral positions at Columbia University under David Blei and Princeton University. His research combines theoretical foundations in statistical machine learning with practical applications in personalization and contextual bandits. Through his work on recommender systems and causal reasoning, he continues to advance the field while developing innovative solutions for real-world applications in content recommendation and user modeling.
Bhaskar MITRA
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1

Course

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Bhaskar Mitra serves as Principal Applied Scientist at Microsoft, where he focuses on machine learning and information retrieval, particularly in neural information retrieval systems. His research combines deep learning approaches with traditional information retrieval methods to improve web search capabilities. As co-author of a foundational book on neural information retrieval and organizer of multiple workshops and tutorials in the field, he has significantly contributed to advancing understanding of neural IR applications. His work spans both theoretical research and practical implementations, particularly in developing and improving search technologies. Through his research and development work, he continues to bridge the gap between academic research and industrial applications in search and information retrieval systems while fostering knowledge sharing through workshops and publications.
Building Recommender Systems: Fundamentals and Practice

Beginner

Skill Level

6 Weeks

Self-paced lessons

Course Cost

₹ 16,167

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.