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Matrix Factorization and Advanced Techniques, University of Minnesota

119 ratings
18 reviews

About 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....

Top reviews


Jul 19, 2017

great courses! They invite a lot of interviews to let me understand the sea of recommend system!


Dec 05, 2017

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

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17 Reviews

By Joeri Kiekens

Mar 28, 2019

Great to have people from industry to talk about recommenders as well. Thanks a lot!

By Ankur Shrivastav

Jan 26, 2019

Great course to understand the fundamentals of recommender systems, as well the diversity & challenges of different recommender systems. The interviews with people from relevant academic fields & industry were particularly useful

I really wish that the programming exercises would be in Python. And that more details on how to implement them (the actual modeling of the algorithms etc) was delved into greater detail.


Aug 25, 2018

It would be better if explaining how to build latent features

By Nicolás Aramayo

Jul 24, 2018

-some videos need better editing

-should go into more mathematical detail for the matrix factorization techniques

By Alberto Guerra

Jun 10, 2018

Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.

By Keshaw Singh

Mar 07, 2018

Based on my experience with the previous courses in this specialization, I was very positively surprised by the amount and depth of material provided in this course. It covers almost everything that is there to be known. The comprehensive interviews are a big plus point. Also, this course provides guidelines as to how to develop, employ and evaluate a recommender system in real life. I would definitely recommend anyone interested in the field to take this course.

By Rahul Gupta

Feb 19, 2018

The Hybrid recommenders i.e. Week 4 needs more explanation especially what is Tensor factorization etc, Week 4 was difficult to grasp. Week 5 and Week 6 was informative especially the LinkedIn video and Learning to Rank: Interview with Xavier Amatriain in which a problem was discussed of having that popularity vs Ranking issue.

Thanks it was quite a lot new things to learn ! My Rating 4.5

By Blake Cole

Jan 13, 2018

Great course. Professors do an excellent job of breaking down this stuff into digestible bits without losing much substance. Highly recommend. You'll need to do some outside exploration and learning though.

By Daniel Pelisek

Jan 07, 2018

Very interesting topic which I was really stoked to learn but unfortunately this course is missing the deep insight into the algorithms, it explains just one algorithm and its variations. The content is overall to little for 6 weeks course and the honor's assignment has very bad task description with errors and lack of validating possibilities.

By Kemal Can Kara

Dec 28, 2017

Few quizes. Easy tests. We need more information on the core techniques.