Recommender Systems Specialization

Master Recommender Systems. Learn to design, build, and evaluate recommender systems for commerce and content.

About This Specialization

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.

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5 courses

Follow the suggested order or choose your own.

Projects

Designed to help you practice and apply the skills you learn.

Certificates

Highlight your new skills on your resume or LinkedIn.

Courses

Intermediate Specialization.: Some related experience required.

COURSE 1
Introduction to Recommender Systems: Non-Personalized and Content-Based
Commitment
4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
Subtitles
English
About the Course
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using
You can choose to take this course only. Learn more.
COURSE 2
Nearest Neighbor Collaborative Filtering
Subtitles
English
About the Course
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with simila
You can choose to take this course only. Learn more.
COURSE 3
Recommender Systems: Evaluation and Metrics
Subtitles
English
About the Course
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity,
You can choose to take this course only. Learn more.
COURSE 4
Matrix Factorization and Advanced Techniques
Subtitles
English
About the Course
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building rec
You can choose to take this course only. Learn more.
COURSE 5
Recommender Systems Capstone
Upcoming session: Oct 1
Commitment
1-3 weeks of study, 3-5 hours per week
Subtitles
English
About the Capstone Project
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a ca
You can choose to take this course only. Learn more.

Creators

The University of Minnesota has been a leader in recommender systems since developing GroupLens, the first automated recommender system in 1993. Today the University continues that leadership with leading research on recommender algorithms, applications, and evaluation.
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.

Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand
Assistant Professor

FAQs

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