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.
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- 5 stars55.11%
- 4 stars29.77%
- 3 stars12%
- 2 stars2.22%
- 1 star0.88%
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.
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!
Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable
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.