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University of Minnesota

Recommender Systems: Evaluation and Metrics

University of Minnesota via Coursera


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.


  • Preface
  • Basic Prediction and Recommendation Metrics
  • Advanced Metrics and Offline Evaluation
  • Online Evaluation
  • Evaluation Design

Taught by

Michael D. Ekstrand and Joseph A Konstan


4.4 rating at Coursera based on 232 ratings

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