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.
This course is part of the Recommender Systems Specialization
About this Course
Syllabus - What you will learn from this course
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
- 5 stars55.50%
- 4 stars29.51%
- 3 stars11.89%
- 2 stars2.20%
- 1 star0.88%
TOP REVIEWS FROM RECOMMENDER SYSTEMS: EVALUATION AND METRICS
Very good. But left out 1 star because one honors assignment did not have the material(base code) to download. Repeated questions were not answered in forum.
It was a great course! Everyone from variety of backgrounds like MS/PhD students or industry professionals that has basic Information Retrieval and ML knowledge could understand the course content.
Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable
A lot of very in detail theories and metrics. I wish it could have more hands on experience.
About the Recommender Systems Specialization
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