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Recommender Systems: Evaluation and Metrics(으)로 돌아가기

Recommender Systems: Evaluation and Metrics, 미네소타 대학교

138개의 평가
22개의 리뷰

About this 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....

최상위 리뷰

대학: LL

Jul 19, 2017

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

필터링 기준:

21개의 리뷰

대학: Gui Ming Tang

Apr 03, 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.

대학: Joeri Kiekens

Mar 27, 2019

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

대학: Anish Sah

Feb 23, 2019

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!

대학: LU WEI

Aug 23, 2018

Confused about some metrics.

대학: Chris Colinsky

Jul 03, 2018

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.

대학: llraphael

Jun 16, 2018

The computer assignment is lack of explanation.

대학: Dhruv Mittal

Jun 15, 2018

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

대학: Caio Henrique Konyosi Miyashiro

May 18, 2018

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.

대학: Yury Zelensky

Mar 29, 2018

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.

대학: Keshaw Singh

Feb 22, 2018

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.