Loved the first part of the course where they introduced many relevant evaluation metrics (root mean square, Spearman, ROC, Precision/Recall, .etc). However, offline/online evaluations were vaguely explained and lacked depth. I really wish there were more concrete, written examples. The final quiz was abstract, weird, and difficult to understand.

That last assignment is great for a better understanding of the metrics.

If you are new to Recommender Systems evaluation, and would like to first know why we do what we do in evaluating a recommender system, go for this course! Each and every approach is explained in vivid details, stripped to the bare essentials so you can see the skeleton of that approach! The only shortcoming, in my opinion was that i felt the codes in honours content in Lenskit could've been further explained. But, all in all, a wonderful place to start!

Confused about some metrics.

not an easy course, specifically the honors track. the information is good, but not presented as well as in the previous two courses. Also there are errors in the honors assignment that make it unnecessarily difficult and you spend a lot of time on irrelevant things.

The computer assignment is lack of explanation.

I was working on a cross-domain recommendation system where i would recommend books to a user whose movie ratings have been given. I made the algorithm but didn't have any idea as to how to evaluate it but this course helped me through. Thanks

the part of offline evaluation is really good and practical as well. However, although knowing online evaluation is a more complex subject, I felt it lacked a little bit how to put all this knowledge in practice.

It is not perfect but best of specialisation so far. It is a little bit philosophical rather than technical and formal, but it was exactly meet my current personal needs. Can not be recommended as a first and only introduction to a topic of an evaluation and metrics of recommender systems.

P.S. Exercises and quizzes, both main and honour, are somewhat eccentric.

My issues about the previous courses in this specialization seem to have been addressed in this one. The assignment in the end is a real good one. The creators of this course have done well to evolve a really thought-provoking and relevant assignment. The course itself helps one develop the appropriate thought process, which comes in handy while deciding upon a metric for a problem at hand.

nice to learn excel statistic

This course was very helpful for giving me a breadth of exposure to various ways to look at evaluating recommender systems. Having faced a very similar problem evaluating a recommender system for a legal document search/suggestion engine (like Google News for lawyers), this gave me a proper "birds eye" perspective on that problem that I wish I had before. We faced exactly the same problem you describe of finding the proper tradeoff between precision and recall, or search vs. discovery.

BUT what is lacking here is teaching us how to go implement these different evaluation metrics in practice. Sadly I don't feel any more equipped to go back to that legal search engine client and guide them toward a very concrete decision about the right metrics to use. I would just come with a mix of new opinions of metrics they should consider -- but how should they choose? what offline evaluation should we do? what online experiment could we run to decide? etc. If you had run us through problem set/assignments involving real-world situations like this, where we had to calculate these different metrics (given sample data) and come up with compelling cases for different metrics to use for evaluation, I would feel otherwise.

That said thank you for your hard work putting the course/specialization together. I hope my feedback helps constructively, but don't see it as criticism. It's because I am very enthusiastic about what you've been teaching me -- and I plan to go implement it for new clients of mine in my Data Science consulting practice (www.waterwaydata.com) -- that I only want the course to be the best it can be for others too.

In addition to the normal number of small errors here and there, the course has too many big errors in the honors track assignments, and no help in the forums. The course appears abandoned.

The videos don't appear to be completely edited, with places where the lecturer says "rewind, I'll start over" or "edit this part out." Also one lecturer in particular will stop mid-sentence as if he has lost the thread of what he was saying, and then finish the sentence with a non-sequitur.

I'm sure they understand the material, but the execution of the presentation is very rough, too rough to continue. I'm bailing out of the specialization after passing 3 courses 100% with honors.

The content is good, interesting but too short for 4 weeks course. Too little new information. The honor assignment was so far the worse. The documentation contain a lot of errors, the description was incomplete.

The course presents the different metrics used for evaluating recommender systems. Moreover, they show many real-life applications where these metrics could be applied and the trade-offs of them. It also includes interviews with experts on the field.

wonderful!!! They teach a lot what I did not expect!

A lot of very in detail theories and metrics. I wish it could have more hands on experience.

这么点内容撑起四周的课程。我不知道课程组织者是怎么想的。Honor assigment 的说明里充斥着巨量的错误。 怀疑其内容没有更新， 依旧是那个旧版本。

Content is not enough for a 4-week course.

Honor assignment need to be updated. There are too many errors in the instruction .

Very Informative. Still, there isn't too much complete evaluation example invovled. It would be a great help if this course can provide some breakdown/design of a recommendaer evaluation system.

Very good. But left out 1 star because one honors assignment did not have the material(base code) to download. Repeated questions were not answered in forum.

The course is interesting because it makes you ask the right questions about recommender systems design. Overfall, there's no great theory behind recommend systems, it's mostly about understanding users' and business' needs, and lecturers do a great job to explain that!