Recommender Systems: Evaluation and Metrics
- 4.4
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Course Summary
This course teaches the metrics used in recommender systems and how to evaluate them. You will learn how to measure the effectiveness of a recommender system and how to optimize it for better performance.Key Learning Points
- Understand the metrics used in recommender systems
- Learn how to evaluate the effectiveness of a recommender system
- Optimize a recommender system for better performance
Related Topics for further study
Learning Outcomes
- Understand the different metrics used in recommender systems
- Learn how to evaluate the effectiveness of a recommender system
- Optimize a recommender system for better performance
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning
- Familiarity with programming languages such as Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science: Machine Learning
- Recommendation Systems
Related Education Paths
Notable People in This Field
- Research Scientist, Google
- Economics of Technology Professor, Stanford University
Related Books
Description
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.
Outline
- Preface
- Introduction to Evaluation and Metrics
- The Goals of Evaluation
- Basic Prediction and Recommendation Metrics
- Hidden Data Evaluation
- Prediction Accuracy Metrics
- Decision Support Metrics
- Rank-Aware Top-N Metrics
- Assignment Intro Video
- Metric Computation Assignment Instructions
- Basic Prediction and Recommendation Metrics Assignment
- Advanced Metrics and Offline Evaluation
- Beyond Basic Evaluation
- Additional Item and List-Based Metrics
- Experimental Protocols
- Unary Data Evaluation
- Temporal Evaluation of Recommenders (Interview with Neal Lathia)
- Programming Assignment Introduction
- Evaluating Recommenders
- Offline Evaluation and Metrics Quiz
- Programming Assignment Quiz
- Online Evaluation
- Introduction to Online Evaluation and User Studies
- Usage Logs and Analysis
- A/B Studies (Field Experiments)
- User-Centered Evaluation (Interview with Bart Knijnenburg)
- Online Evaluation Quiz
- Evaluation Design
- Matching Evaluation to the Problem/Challenge
- Case Examples
- Assignment Intro Video
- Intro to Assignment: Evaluation Design Cases
- Quiz Debrief
- Assignment: Evaluation Design Cases
Summary of User Reviews
Learn about recommender metrics in this comprehensive course on Coursera. Students have given this course high ratings and have praised the instructor's ability to explain complex concepts in an easy-to-understand way.Key Aspect Users Liked About This Course
Instructor's ability to explain complex concepts in an easy-to-understand wayPros from User Reviews
- Clear and concise explanations of complex topics
- Instructor is knowledgeable and engaging
- Real-world examples are provided to illustrate concepts
- Assignments and quizzes are challenging and help reinforce learning
Cons from User Reviews
- Course can be challenging for beginners with no background in statistics
- Some students feel that the course material could be more organized
- Course may be too technical for some learners
Michael D. Ekstrand
4.4 Raiting
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