What Are Performance Metrics?
March 17, 2025
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This course is part of Recommender Systems Specialization
Instructors: Michael D. Ekstrand
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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.
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We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
University of Minnesota
Specialization
EIT Digital
Course
Sungkyunkwan University
Course
University of Minnesota
Course
233 reviews
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Reviewed on Jul 18, 2017
wonderful!!! They teach a lot what I did not expect!
Reviewed on 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.
Reviewed on Jul 15, 2017
A lot of very in detail theories and metrics. I wish it could have more hands on experience.
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