Recommender Systems: Evaluation and Metrics

Overview

Delve into the intricate world of recommender systems with the University of Illinois at Urbana-Champaign's comprehensive course, now available on Coursera. This detailed curriculum is designed to equip you with the necessary skills to expertly evaluate recommender systems through an in-depth understanding of a broad spectrum of metrics.

Participants will explore various metric families that assess aspects like prediction accuracy, rank accuracy, decision support, amidst others such as diversity, product coverage, and the elusive quality of serendipity. This course articulates how distinct metrics underscore diverse user and business objectives, paving the way to tailor recommender systems that align with specific needs.

Furthermore, learners will be versed in conducting thorough offline evaluations, including data preparation, sampling methods, and result aggregation, alongside an introduction to online (experimental) evaluations. Upon completion, you'll possess a robust toolkit for comparing recommender system alternatives across a myriad of applications.

This program is categorized under Artificial Intelligence Courses and Recommender Systems Courses, marking a pivotal learning journey for those keen on mastering the art and science of recommendation in the digital age.

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Taught by

Michael D. Ekstrand and Joseph A Konstan

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Subjects