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

4.4
stars
644 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

YW

Nov 2, 2016

I think this is an amazing course for beginners who are interested in recommender systems, I strongly recommend this course to the students and engineers who are working on recommender systems.

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.

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126 - 135 of 135 Reviews for Introduction to Recommender Systems: Non-Personalized and Content-Based

By Aditya C

Jul 21, 2024

Please re-update the course to be flexible to current data scientists. I could not do the honors unit because of my current java version which was incompatible with the gradle format given by the honors content. Perhaps switch to Python

By Paulo E d V

Dec 8, 2016

Ok, it's an introduction, but it could at least show us some math or pseudocodes. A part from that, the course is really awesome. Well structured classes, good explanations and incredible interviews

By Yan F

Sep 19, 2021

The course was generally ok, but can benefit from better lecture structure. For example, the general topic can move upfront, with more mathematical illustration on how content filtering is done.

By Ruth B

Aug 13, 2017

Not bad for an introduction, but I would have prefered it to be more technical

By Lucas B

Sep 4, 2019

Was expecting programming activities in Python or R, not in Java =/

By Kevin J A D

Jan 28, 2022

The core lectures are really good, the honours track is outdated.

By Priyamvada S

Dec 6, 2021

doesnt cover collaborative; rest is fine

By ­박민혜 / 학 / 데

Feb 26, 2020

수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.

By Leonardo R

Dec 26, 2023

The content itself is great, but the course is outdated and kept without a proper maintenance staff to answer the discussion forum. The course should have moved to Python instead of Java. Some videos have sound issues.

By Roman O

Nov 5, 2021

Bad, pretty bad. Too theoretical. I've got felling all the time that a LOT of terms I hearing first time and felling that they was spoken somewhere else, but not here. It is horrible frankenstein of scattered knowledge that lectors pretended to call a 'cource'. I've got more knowledge just from reading few chapters on recomendation systems book, than listening to this.