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Learner Reviews & Feedback for Introduction to Recommender Systems: Non-Personalized and Content-Based by University of Minnesota

4.4
stars
637 ratings

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 summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems....

Top reviews

BS

Feb 12, 2019

One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.

DP

Dec 7, 2017

Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).

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51 - 75 of 134 Reviews for Introduction to Recommender Systems: Non-Personalized and Content-Based

By Abhijith R

Aug 30, 2020

Great intro to recommendation systems, the course is well structured and engaging to all students of different backgrounds.

By Тефикова А Р

Oct 5, 2016

Курс очень понравился, спасибо большое за такую уникальную возможность вникнуть в суть рекомендательных систем!

By Saurabh D

Aug 13, 2023

Great course.

I would encourage the authors of the course to replace Java with Python in the Honors track

By Chris C

Jul 6, 2021

Excellent content, great structured frameworks to understand when / why to use different recommenders

By Patrick D

Jun 25, 2017

Great, thorough introduction with tracks for both Java programmers and non-programmers.

By Pankaj M

Dec 20, 2022

Well designed introduction to the formal concepts and analysis of Recommender systems

By Kevin R

Oct 8, 2017

Well-designed assignments and instructive programming exercises in the honors track.

By Ashwin R

Jun 26, 2017

An excellent in-depth introduction into the concepts around recommendation systems!

By Santiago F

Feb 1, 2021

Muy claro y de gran ayuda para los que se estén introduciendo en el tema.

By Xinzhi Z

Jul 17, 2019

Great course. I really appreciated the efforts spent by the course team.

By 王涛

Apr 10, 2019

Really Good! I think it will be helpful to me and take a job for me!

By Light0617

Jul 18, 2017

great!! Let me better understand the research and practical fields!

By Sushmita B

Jun 7, 2020

The course is very good and the course instructor is excellent .

By Luis D F R

Apr 17, 2017

Really good course to get started with recommendation systems!

By Apurva D

Aug 3, 2017

Awesome content...loved the industry expert interviews....

By Dan T

Oct 31, 2017

great overview of the breadth of material to get started

By Sreenath A

Jun 29, 2017

Excellent course taught in simple language.

By Biswa G S

Mar 28, 2018

Good overview on the recommend-er system.

By Sherry L

Nov 21, 2017

great professors and inspiring lectures!

By 王嘉奕

Nov 6, 2019

Excellent course which helps me a lot.

By Su L

Aug 23, 2019

great course, learnt a lot, thanks!

By Fernando C C

Nov 7, 2016

pues esta bien chido el curso

By Son M

Jan 19, 2019

good exercises & lectures

By BEBIN K R

Sep 17, 2020

Wonderful experience

By Julia E

Nov 8, 2017

Thank you very much!