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Курс от University of Minnesota
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Recommender Systems: Evaluation and Metrics
Курс от University of MinnesotaО курсе
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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Программа курса
5 модулей · 29 учебных материалов
01Preface2 материалов
02Basic Prediction and Recommendation Metrics7 материалов
Basic Prediction and Recommendation Metrics
Hidden Data EvaluationВидеоPrediction Accuracy MetricsВидеоУчитесь у экспертов
Michael D. Ekstrand
Assistant Professor
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor