Chevron Left
Back to Introduction to Recommender Systems: Non-Personalized and Content-Based

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

626 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


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.


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).

Filter by:

1 - 25 of 130 Reviews for Introduction to Recommender Systems: Non-Personalized and Content-Based

By Benjamin S S

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.

By Ali K

Jan 2, 2018

well one thing I am struggling with programming in JAVA. Would not it be handy to have option to do assignment using languages like python/R? which are basically language of choice for data scientists and also easy to have grasp on for newbies. one more thing some time I just get stuck and felt like now way out. I did not get any answer/help form posts on the forum .

By Dennis D

Jan 1, 2021

Very good course, Very well delivered and paced. Could benefit from an update?

(1) No Python honors option? (e.g. per LensKit platform used has moved to Python since 2018)

....this is why I did not take up the honors option

(2) Deep Learning based RS?

(3) 2013 RS paper cited not accessible and newer RecSys papers since?

By Siddhartha S B

May 13, 2020

Honors track should be in Python. The subjective questions of the evaluation lacks clarity in some cases.

By Andrés C C

Aug 7, 2021

- Too slow, too much wandering around instead of focusing on the concepts - Outdated coding exercises that don't integrate properly with modern IDEs - Too much emphasis on spreadsheets, too little emphasis on coding - The coding exercises made in Java put too much emphasis on unrelated stuff, not just because of the language, but because of how they are prepared.

By Nicolás A

Jun 28, 2018

Too basic and too repetitive (the videos could be half as long)


Mar 29, 2020

Coding assignment should not be just restricted to java

By Oleg P

May 24, 2020

There is no math in this course and it does not use Python. Therefore this course does a terrible job of preparing you for interview questions on Recommender systems. Personally I thought this course was a waste of my time and money. However the final excel exercise actually had some useful information, but it was only a 10 minute exercise after many hours of useless lectures. I could have done the same exercise for free.

By Ellinor G

Apr 16, 2021

The now old trick of quite many data courses on Coursera of lulling it through the instructional material and only raising the bar when it comes to the quizzes is abundant in this course.

If you are interested in 25 minute -videos (??!) showing how the instructor chooses a lightbulb on Amazon then you will surely like this course. If not, there's better elsewhere.

By Tash B

Jun 27, 2018

Fantastic course. Lecturers have extensive experience in this field. Lectures include interviews with people who have successfully implemented recommender systems in their products or who are researching the permutations, challenges and extensions to recommender system development. Not only does the course provide the chance to build your own recommender systems (optional) but also highlights the complexities and opportunities for refining and improving recommendations. I highly recommend this course to anyone building recommendation systems.

By Seema P

Jan 7, 2017

Exceptional quality.The course content is comprehensive and practical enough applied at workplaces.

Guest lectures are super helpful and assignments are very practical yet make you think.

Thank you Coursera and Minnesota professors for this amazing course and wonderful opportunity for people like me with no background in recommendation systems learn the best research methods and practices in this field.

By Arif L

Jun 14, 2020

I am confused using Java for programming, it is better using python or R in the next course

By Abhinandan D

Nov 9, 2020

Big fan of Prof. Konstan ! Amazing how he subtly puts a joke or two in middle of explaining something. I feel the course could benefit from a little more programming content / assignments, and some more math behind the ideas presented. It presents really good philosophy behind recommendation systems and definitely got me hooked into this amazing field. However the course itself is very diluted and could use some math.

By Lucia P

Jul 29, 2020

Interesting course, good overview, and presentation of the topic to those who are not familiar with RS.

Could have been 5 stars if the "developer" modules were available on Python. That's a big fail.

By Anil S

Aug 28, 2020

Would be nice to complete the honours track using python.


Apr 6, 2020

The pace is too slow. Lectures spend lots of time on examples, and all kinds of possible variables.

These make stories very long, and badly-structured. It may be better to introduce only one concept at any moment, and discuss the problem and the solution immediately after mentioning the concept. That will help students to focus on the point and get it right sooner. It's good to combine all these concepts together after we've known everything, but not at the very beginning.

Also the programming assignment is really bad. As a CS student, I spent almost 90% of time on realizing the architecture, tools and libraries. I don't think these third-party libraries are helpful here. The same tasks can be implemented by pure Java code even more efficiently (for coding). Most non-CS students will find it difficult to use, while CS students can learn only little from the assignment since the core ideas to implement are far too easy.

I can feel how much knowledge lectures expect us to get from this lecture, but it really needs a rebuilding. Maybe trying to put a self limitation on video length will be a good start. Expressing a brief idea in a short video, and allowing students to consume one video even with only a piece of time, should be one of the most appealing part in flip-classroom.

By Maksym Z

Jan 29, 2017


Some useful terminology if you want to ever communicate with someone who does recommender systems.


Very diluted content.

Mostly large text slides with the presenter talking in a monotone voice.

Programming exercises are done in Java and require deploying an IDE + an unused open source project developed by the authors. Hint to the authors: use Python, R or Octave like everyone does.

Some of the questionaries are ambiguous.

By Jon H

Feb 14, 2019

The content of this course is solid. It's a good introduction to content based and non-personailzed recommender systems. However, the presentation is poor. The course is largely based around videos which appear to be single takes. Snappier, well edited videos would have been better and, as a result, I often found myself skimming the transcripts rather than watching the videos.

By Sharat M

Nov 9, 2016

As an introductory course, the content was good. But I wish the approach was more analytical and more hands on. Rather than history of Recommender systems & what happened in the 90s, I would have been happier if the course was able to throw light on the latest stuff in this field, the latest mathematical techniques etc.

By Faizan A

Mar 1, 2017

The assignments are not very relevant to what is being taught. Java 7 instead of Java 8 makes things too verbose. Lenskit is painful to use and in the week 4 Honors assignment its just impossible to get the results desired by the grader. I would suggest the Teaching team to use R/python scikit instead of Java

By Joeri K

Mar 23, 2019

It would be nice to have a hierarchical overview of the recommender systems. It's easy to get lost which is a subcategory of which. Thanks for the course!

By Artur

Sep 12, 2017

The introduction is very slow in my opinion. Hopefully, it will pick up the pace in the later modules.

By Md. S R

Jan 5, 2019

The lecturer were very lengthy, at least for me. I find it difficult to concentrate.

By Михаил Б

Dec 30, 2019

I feel like the course could've been condensed to 1 or 2 weeks max

By Alex B

Aug 25, 2019

This course mostly works. Contains a lot of wasted video time where no information is communicated. Uses simplistic tools that don't scale to data applications or otherwise dated tools not really used by data scientists or machine learning engineers making exercises either simplistic or a waste of time. Better than other courses in the series in that the assignments are legible.