i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material

Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.

Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!

everything best. But technical support in Forum and when a student needs help when he is learning in Vienna alone is the worst

thanks very much !

Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!

Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved

Very satisfied to do this, the videos are too long, very good quality and a lot of practical information.

I love it!

I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)

Provides a good overview of item based and user based collaborative filtering approaches.

Awesome Professors!Great Material.Very thankful to Coursera for providing this course.

Loved it...many thanks Prof. Joe for bringing this content to Coursera

a great class, I learned some insight in these algorithms

Great learning experience about collaborative filtering!

Exercises take time but really helpful.

Very good assignments, honors track.

in-depth and well-made to follow

Nice course!

excellent

very nice

Thank you for this course -- it opened my eyes to the universal applicability of recommender systems in tech applications.

My feedback is that you could do more to tie the *implementation* to the theory and real-life applications you discuss. You have many great lectures talking about how these systems were implemented, qualitative differences, subtle differences, and interviewing people to give us perspectives. But then the videos on implementation (including working through the equations) are pretty sparse and short. I felt like I'm "on my own" to figure out how to go implement these in real life. The problem sets cover one test case, and that's it. I think you could update the lectures to focus more on different algorithms / equations in different scenarios, rather than just talking qualitatively about them.

Regardless thank you! I deeply appreciate this course and what you've done. I plan to help my Consulting clients directly based on what I learned from you.

The topics I am interested in covered by people who definitely has related expertise. But overall quality of the teaching materials expected to be higher. Forum is also a little bit deserted, although contains some critical hints to pass the assignments (such a hints worth to be included in the assignment descriptions itself). I want to support the course, and it is pity to give it only 4 of 5 stars, but I really expect more quality from the course I paid for.

Excellent course providing not only the knowledge of algorithms but also useful insights into developing and maintaining recommender systems. Only thing that could use some work is the assignments. Spreadsheet assignment in week 4 is poorly designed (as evidenced by many forum threads with people not knowing what is it that the authors actually want). Other than that, that was an extremely helpful course.

Overall , it is a very interesting course.

But I would like to say , that there are too many interviews. I think that it is a little bit difficult for some non-native speaker to understand the main and important things from the interview, because some interviewers talked in a very loose way. So I would suggest our teacher , to summarize the main points of those interview in a better way .

Diverse content that helps in understanding the basic concepts of collaborative filtering. Interviews with people specializing in different nuances of collaborative filteering were very useful.

Some thoughts on what could be improved

Pace of narration. It can be faster

More exercises are needed to get more familiar with the concepts. Each lecture should have a exercise (not just a quiz)

Rather non-technical, interesting general information, plus voluntary programming assignment which I personally found little bit "bulky". More effort I spent to get familiar with the library than to actually use the collaborative filtering algorithms.