Online Course
Recommender Systems: Evaluation and Metrics
University of Minnesota via Coursera
Overview
Class Central Tips
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.
Syllabus
Preface
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Online Evaluation
Evaluation Design
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Online Evaluation
Evaluation Design
Taught by
Michael D. Ekstrand and Joseph A Konstan
Charts
- #3 in Subjects / Machine Learning / Recommender Systems
Tags
Related Courses
-
Recommender Systems
University of Minnesota
-
Introduction to Recommender Systems: Non-Personalized and Content-Based
University of Minnesota
-
Recommender Systems Capstone
University of Minnesota
-
Matrix Factorization and Advanced Techniques
University of Minnesota
-
Nearest Neighbor Collaborative Filtering
University of Minnesota
2.0 -
Basic Recommender Systems
EIT Digital
Reviews
0.0 rating, based on 0 reviews
Never Stop Learning!
