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.
Recommender Systems: Evaluation and Metrics
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Recommender Systems: Evaluation and Metrics
This course is part of Recommender Systems Specialization
Instructors: Michael D. Ekstrand
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4.4
(233 reviews)
7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
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Assessments
5 assignments
Taught in English
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There are 5 modules in this course
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2 videos
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