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.
이 강좌에 대하여
- 5 stars55.30%
- 4 stars29.64%
- 3 stars11.94%
- 2 stars2.21%
- 1 star0.88%
RECOMMENDER SYSTEMS: EVALUATION AND METRICS의 최상위 리뷰
wonderful!!! They teach a lot what I did not expect!
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.
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.
추천 시스템 특화 과정 정보
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.