Recommender Systems: Behind the Screen from edX

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's inside

Learning objectives

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.
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.
At the end of the mooc, participants should be able to:

Syllabus

MODULE 1 Machine Learning for Recommender Systems

Score Models

Practical Aspects

MODULE TUTORIAL Matrix Factorization

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on supervised learning and doesn't explore advanced concepts like deep learning

Covers evaluation offline and online leveraging concrete examples

Provides hands-on experience through tutorials and practical sessions

Offers industry-relevant examples and discusses real-world applications

Covers the latest trends and research in recommender systems, including contextual bandits and fairness

Taught by experienced industry experts and academics with proven track records

Reviews summary

Comprehensive recommender systems overview

According to students, 'Recommender Systems: Behind the Screen' offers a largely positive and strong theoretical foundation in recommender systems. Learners particularly appreciate the diverse expert instructors who provide insightful lectures and comprehensive coverage of topics, including the crucial module on fairness and discrimination. While the course is highly valued for its conceptual understanding and academic depth, some industry-focused learners note a limited focus on hands-on coding practice in the core curriculum, suggesting external practice or the verified certificate's self-practice module may be necessary for those aiming for direct professional application.

Ideal for academic grounding, but industry professionals may need supplementary practice.

"It's a good academic overview, but perhaps not enough for direct application in a professional setting without significant extra effort."

"The course is great for academic grounding, but industry professionals seeking hands-on application might find it limited."

"As an aspiring ML engineer, I needed more coding challenges and real-world datasets to work with."

"It felt more like a series of academic lectures than a practical skill-building course."

The self-practice module for verified certificate holders enhances practical learning.

"The self-practice module (with verified certificate) was truly beneficial for hands-on experience."

"The 'self-practice module' for verified cert was good, but it would have been better if more hands-on coding was integrated throughout the free version too."

Features multiple international experts providing diverse and insightful perspectives.

"The lectures by the various experts are insightful."

"The expert instructors brought diverse perspectives."

"The diverse set of lecturers was a big plus, bringing different angles to the subject."

"The instructors are clearly experts in their fields."

Provides comprehensive conceptual understanding of recommender systems.

"This course provides a solid introduction to recommender systems. The content is well-structured..."

"Absolutely fantastic! As someone with a background in data science but new to recommender systems, this course provided an excellent foundation."

"The theoretical aspects are strong, offering a comprehensive overview."

"It gave me a strong conceptual understanding that I can now build upon."

While strong in theory, it offers insufficient hands-on coding for direct industry use.

"I felt the practical coding exercises were a bit light; I had to seek out additional practice..."

"I found the practical implementation lacking. The 'tutorial' was more a demonstration than an interactive coding session."

"Disappointed with the practical side... I needed more coding challenges and real-world datasets to work with."

"Not practical enough for my needs as a software developer. I wish there were more assignments that involved writing code from scratch."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Recommender Systems: Behind the Screen with these activities:

Organize a Study Group on Recommender Systems

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Discussing course material with peers can improve recall and retention.

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Find a group of classmates who are also taking the course
Meet regularly to discuss the course material
Work together on assignments and projects

Build a Recommender System with Scikit-learn

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This tutorial will help you gain hands-on experience with one of the most popular Python libraries for building recommender systems.

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Install Scikit-learn
Load a dataset
Create a recommender system model
Evaluate the performance of the model

Solve Recommender System Code Challenges

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Solving coding challenges will further your ability to implement recommender systems.

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Go to a coding challenge website, such as LeetCode or HackerRank
Search for recommender system challenges
Solve as many challenges as you can

Three other activities

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Practice Matrix Factorization for Recommender Systems

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Understanding how to apply matrix factorization to recommender systems

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Learn about matrix factorization
Implement matrix factorization for recommender systems
Evaluate the performance of your model

Follow PracticalTensorflow Tutorial

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Stepping through this tutorial will enhance the likelihood of understanding how to build recommender systems with this open source machine learning library.

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Go to the PracticalTensorflow website
Click on the Recommender System with TensorFlow tutorial
Complete each exercise in the tutorial

Design and Write a Recommender System Proposal

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Creating a proposal for a recommender system will help you to apply the concepts of what you are learning.

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Identify a potential use case for a recommender system
Describe the data that would be used to train the recommender system
Choose a recommender system algorithm and justify your choice
Describe how you would evaluate the performance of the recommender system
Write a proposal that outlines your plan

Career center

Learners who complete Recommender Systems: Behind the Screen will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data. They use their findings to help businesses make better decisions. This course can help you develop the skills you need to succeed as a Data Scientist, including machine learning, data mining, and statistical analysis. With a strong understanding of recommender systems, you can specialize in developing and deploying these systems to enhance user experience and engagement on digital platforms.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their expertise in machine learning algorithms and techniques to solve complex problems. This course can help you develop the skills you need to succeed as a Machine Learning Engineer, including machine learning, data mining, and statistical analysis. It will help you gain experience in applying machine learning to real-world problems, such as recommender systems.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. They use their skills in programming languages and software development tools to create software that meets the needs of users. This course can help you develop the programming skills and machine learning knowledge that you need to succeed as a Software Engineer. It will also help you gain experience with recommender systems and how they can be implemented in software.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts are responsible for collecting, analyzing, and interpreting data. They use their findings to help businesses make better decisions. This course can help you develop the skills you need to succeed as a Data Analyst, including machine learning, data mining, and statistical analysis. The course will help you understand how recommender systems can be used to improve customer engagement and satisfaction.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They use their skills in data analysis and problem-solving to help businesses make better decisions. This course can help you develop the skills you need to succeed as a Business Analyst, including machine learning, data mining, and statistical analysis. The course will also provide you with the knowledge of recommender systems and how they can be used to improve business outcomes.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for managing the development and launch of new products. They use their skills in market research, product development, and marketing to create products that meet the needs of users. This course can help you develop the skills you need to succeed as a Product Manager, including machine learning, data mining, and statistical analysis. It will also help you gain experience with recommender systems and how they can be used to improve product recommendations.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They use their skills in market research, marketing strategy, and advertising to promote products and services. This course can help you develop the skills you need to succeed as a Marketing Manager, including machine learning, data mining, and statistical analysis. It will also help you gain experience with recommender systems and how they can be used to improve customer targeting and engagement.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for leading and motivating sales teams. They use their skills in sales strategy, negotiation, and customer relationship management to close deals and generate revenue. This course can help you develop the skills you need to succeed as a Sales Manager, including machine learning, data mining, and statistical analysis. The course will help you understand how recommender systems can be used to improve sales forecasting and lead generation.

See salaries and explore the career path for Sales Manager

Customer Success Manager

Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. They use their skills in customer service, problem-solving, and relationship management to build strong relationships with customers. This course can help you develop the skills you need to succeed as a Customer Success Manager, including machine learning, data mining, and statistical analysis. It will also help you gain experience with recommender systems and how they can be used to improve customer satisfaction and retention.

See salaries and explore the career path for Customer Success Manager

Operations Manager

Operations Managers are responsible for planning, coordinating, and executing business operations. They use their skills in operations management, supply chain management, and project management to ensure that businesses run smoothly. This course can help you develop the skills you need to succeed as an Operations Manager, including machine learning, data mining, and statistical analysis. It will also help you gain experience with recommender systems and how they can be used to improve operational efficiency and productivity.

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Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations on investments. They use their skills in financial modeling, data analysis, and risk management to help investors make informed decisions. This course may be useful for those who want to pursue a career as a Financial Analyst, as it will provide them with a strong foundation in machine learning and data analysis.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries are responsible for assessing and managing risk. They use their skills in mathematics, statistics, and finance to develop and implement risk management strategies. This course may be useful for those who want to pursue a career as an Actuary, as it will provide them with a strong foundation in machine learning and data analysis.

See salaries and explore the career path for Actuary

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use their skills in statistical modeling, data analysis, and probability theory to solve problems and make informed decisions. This course may be useful for those who want to pursue a career as a Statistician, as it will provide them with a strong foundation in machine learning and data analysis.

See salaries and explore the career path for Statistician