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Learner Reviews & Feedback for Recommender Systems: Evaluation and Metrics by University of Minnesota

4.4
stars
232 ratings

About the Course

In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses....

Top reviews

MB

Dec 4, 2022

It was a great course! Everyone from variety of backgrounds like MS/PhD students or industry professionals that has basic Information Retrieval and ML knowledge could understand the course content.

NS

Dec 13, 2019

Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable

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26 - 32 of 32 Reviews for Recommender Systems: Evaluation and Metrics

By Jim T

Apr 3, 2019

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.

By Alex B

Aug 27, 2019

The first two weeks are fantastic, up until evaluation metrics stop being covered. After that nothing concrete is said and very little is to be learned. Skip after that.

By llraphael

Jun 16, 2018

The computer assignment is lack of explanation.

By LU W

Aug 23, 2018

Confused about some metrics.

By Maxwell's D

Jan 15, 2018

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.

By Daniel P

Dec 23, 2017

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.

By Siwei Y

Jul 3, 2017

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 .