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.
Course 3 of 5 in the Recommender Systems Specialization.
Basic Prediction and Recommendation Metrics
Graded: Basic Prediction and Recommendation Metrics Assignment
Advanced Metrics and Offline Evaluation (Part 1)
Graded: Offline Evaluation and Metrics Quiz
Graded: Online Evaluation Quiz
Graded: Assignment: Evaluation Design Cases
ENROLL IN COURSE