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Bewertung und Feedback des Lernenden für Recommender Systems: Evaluation and Metrics von University of Minnesota

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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-Bewertungen

NS

13. Dez. 2019

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

LL

18. Juli 2017

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

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