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.
課程信息
提供方

明尼苏达大学
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
教學大綱 - 您將從這門課程中學到什麼
Preface
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Online Evaluation
Evaluation Design
審閱
來自RECOMMENDER SYSTEMS: EVALUATION AND METRICS的熱門評論
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.
wonderful!!! They teach a lot what I did not expect!
關於 推荐系统 專項課程
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.

常見問題
我什么时候能够访问课程视频和作业?
我订阅此专项课程后会得到什么?
Is financial aid available?
完成课程后,我会获得大学学分吗?
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