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
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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This course is part of the Recommender Systems Specialization
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There are 5 modules in this course
What's included
2 videos
What's included
5 videos1 reading1 assignment
What's included
6 videos1 reading2 assignments
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4 videos1 assignment
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3 videos2 readings1 assignment
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4.9 (8 ratings)Offered by
Recommended if you're interested in Machine Learning
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LL
5
Reviewed on Jul 18, 2017
wonderful!!! They teach a lot what I did not expect!
NS
5
Reviewed on Dec 13, 2019
Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable
MB
5
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.
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