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Recommender Systems: Evaluation and Metrics

Learn advanced techniques for evaluating recommender systems, including metrics for prediction accuracy, rank accuracy, and decision support.

Learn advanced techniques for evaluating recommender systems, including metrics for prediction accuracy, rank accuracy, and decision support.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Recommender Systems Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.4

(233 ratings)

13,397 already enrolled

English

پښتو, বাংলা, اردو, 2 more

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Recommender Systems: Evaluation and Metrics

This course includes

7 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Master various families of evaluation metrics for recommender systems

  • Implement offline evaluation protocols and sampling techniques

  • Design and analyze A/B tests for online evaluation

  • Assess recommender systems for accuracy, diversity, and user satisfaction

  • Match evaluation approaches to specific business and user goals

Skills you'll gain

Recommender Systems
Prediction Metrics
A/B Testing
Evaluation Design
Machine Learning
Data Analysis
Online Evaluation
Offline Evaluation
User Studies
Statistical Analysis

This course includes:

4 Hours PreRecorded video

5 assignments

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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There are 5 modules in this course

This comprehensive course focuses on the evaluation of recommender systems, covering both theoretical frameworks and practical implementation. Students learn various evaluation metrics including prediction accuracy, rank accuracy, and decision support measures. The curriculum encompasses offline evaluation protocols, online experimentation through A/B testing, and user-centered evaluation approaches. Through practical assignments and case studies, participants gain hands-on experience in designing and conducting rigorous evaluations of recommender systems.

Preface

Module 1 · 13 Minutes to complete

Basic Prediction and Recommendation Metrics

Module 2 · 1 Hours to complete

Advanced Metrics and Offline Evaluation

Module 3 · 2 Hours to complete

Online Evaluation

Module 4 · 1 Hours to complete

Evaluation Design

Module 5 · 1 Hours to complete

Fee Structure

Instructors

Joseph A Konstan
Joseph A Konstan

4.7 rating

237 Reviews

2,11,579 Students

11 Courses

Leading Expert in Human-Computer Interaction at the University of Minnesota

Dr. Joseph A. Konstan is a distinguished professor in the Department of Computer Science and Engineering at the University of Minnesota, where he holds the title of Distinguished McKnight University Professor and Distinguished University Teaching Professor. His extensive research focuses on human-computer interaction, particularly in the areas of recommender systems, social computing, and public health applications. Notably, his work on the GroupLens Recommender System earned him the prestigious ACM Software Systems Award in 2010.Dr. Konstan received his A.B. from Harvard University and both his M.S. and Ph.D. from the University of California, Berkeley. He is recognized for his contributions to education through various teaching awards and has delivered numerous webinars and short courses on topics such as recommender systems and ethical issues in social computing. As a Fellow of the ACM, IEEE, and AAAS, he has also served as President of ACM SIGCHI and has chaired several major conferences in the field. His courses on Coursera include "Introduction to Recommender Systems," "Evaluating User Interfaces," and "User Research and Design," aimed at equipping students with essential skills in user experience and system design.

Michael D. Ekstrand
Michael D. Ekstrand

4.6 rating

60 Reviews

1,09,681 Students

6 Courses

Expert in Recommender Systems at Boise State University

Dr. Michael D. Ekstrand is an Assistant Professor in the Department of Computer Science at Boise State University, where he focuses on evaluating and understanding recommender systems in relation to user goals and information needs. His research emphasizes supporting reproducible research in the field of recommender systems. Dr. Ekstrand is also the lead developer of LensKit, an open-source toolkit designed for building, researching, and studying recommender systems.He teaches several courses on Coursera, including "Introduction to Recommender Systems: Non-Personalized and Content-Based," "Matrix Factorization and Advanced Techniques," and "Recommender Systems Capstone." His work aims to enhance the effectiveness of recommendation algorithms while ensuring they meet user needs.Dr. Ekstrand earned his Ph.D. from the University of Minnesota and has contributed significantly to the field through his research and development efforts. He is actively involved in various academic initiatives and has presented at numerous conferences related to information science and recommender systems.

Recommender Systems: Evaluation and Metrics

This course includes

7 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.