This course is part of the Recommender Systems Specialization

4.3
stars
185 ratings

Michael D. Ekstrand +1 more instructor

14,669 already enrolled

Offered By

Recommender Systems SpecializationUniversity of Minnesota

About this Course

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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 recommender systems based on reducing the dimensionality of the user-product preference space.  Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.

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Course 4 of 5 in the

Recommender Systems Specialization

Approx. 15 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 5 in the

Recommender Systems Specialization

Approx. 15 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.87/5 (8 Ratings)

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

105,189 Learners

6 Courses

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

191,999 Learners

11 Courses

Offered by

University of Minnesota

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.

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Syllabus - What you will learn from this course

Week1

Week 1

4 minutes to complete

Preface

4 minutes to complete

1 video (Total 4 min)

Week2

Week 2

1 hour to complete

Matrix Factorization (Part 1)

1 hour to complete

5 videos (Total 70 min), 1 reading

Week3

Week 3

6 hours to complete

Matrix Factorization (Part 2)

6 hours to complete

2 videos (Total 15 min), 2 readings, 6 quizzes

Week4

Week 4

2 hours to complete

Hybrid Recommenders

2 hours to complete

6 videos (Total 96 min)

Reviews

4.3

28 reviews

5 stars
53.51%
4 stars
32.97%
3 stars
8.10%
2 stars
4.32%
1 star
1.08%

TOP REVIEWS FROM MATRIX FACTORIZATION AND ADVANCED TECHNIQUES

by DDJan 9, 2021

Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.

by SLSep 11, 2019

It will be great, if we can do honor's track with Python or R

by AAAug 13, 2017

Interview with Francesco Ricci

is very knowledgeable about context aware Recommender System.

by NLApr 23, 2020

The content is really good, but overall the interviews with experts in the field are the best of this course.

View all reviews

About the Recommender Systems Specialization

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.

This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Matrix Factorization and Advanced Techniques

About this Course