Matrix Factorization and Advanced Techniques

Seize the savings! Get 40% off 3 months of Coursera Plus and full access to thousands of courses.

Matrix Factorization and Advanced Techniques

This course is part of Recommender Systems Specialization

Instructors: Michael D. Ekstrand

15,961 already enrolled

Included with

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

190 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

190 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Recommender Systems Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 6 modules in this 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 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.

What's included

1 video

This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.

What's included

5 videos1 reading

What's included

2 videos2 readings5 assignments1 programming assignment

2 videos Total 15 minutes

Assignment Introduction 8 minutes
Programming Matrix Factorization 7 minutes

2 readings Total 20 minutes

Assignment Instructions 10 minutes
Intro - Programming Matrix Factorization 10 minutes

5 assignments Total 68 minutes

Matrix Factorization Assignment Part l 10 minutes
Matrix Factorization Assignment Part ll 10 minutes
Matrix Factorization Assignment Part lll 10 minutes
Matrix Factorization Quiz 8 minutes
SVD Programming Eval Quiz 30 minutes

1 programming assignment Total 180 minutes

Programming SVD 180 minutes

This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.

What's included

6 videos

What's included

3 videos

What's included

7 videos1 reading2 assignments1 programming assignment

7 videos Total 133 minutes

Context-Aware Recommendation I : Interview with Francesco Ricci 21 minutes
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1) 22 minutes
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2) 18 minutes
Industry Practical Issues: Inteview with Anmol Bhasin 27 minutes
Recommending Music - Interview with Paul Lamere 14 minutes
Specialization Wrap Up 22 minutes
Programming Hybrids & Learning to Rank 9 minutes

1 reading Total 10 minutes

Programming Hybrids and Machine Learning Description 10 minutes

2 assignments Total 42 minutes

Hybrid and Advanced Techniques Quiz 12 minutes
Honors Hybrid Assignment Evaluation Quiz 30 minutes

1 programming assignment Total 180 minutes

Programming Hybrids and Learning-to-Rank 180 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings

(8 ratings)

Michael D. Ekstrand

University of Minnesota

6 Courses 112,135 learners

Joseph A Konstan

University of Minnesota

11 Courses 220,093 learners

Offered by

University of Minnesota

Explore more from Machine Learning

28DIGITAL
Advanced Recommender Systems
Course
Status: Preview
University of Minnesota
Matrix Methods
Course
Status: Preview
Sungkyunkwan University
Recommender Systems
Course
Status: Free Trial
Packt
Recommender Systems with Machine Learning
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
54.73%
4 stars
32.10%
3 stars
7.89%
2 stars
4.21%
1 star
1.05%

Showing 3 of 190

Reviewed on Dec 4, 2017

Awesome course especially for those doing Ph.D in recommender systems

Reviewed on Jan 2, 2021

Really enjoyed the course!One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)

Reviewed on Sep 11, 2019

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

View more reviews

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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.

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.