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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課程信息
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學習Course的學生是
- Data Scientists
- Machine Learning Engineers
- Chief Technology Officers (CTOs)
- Research Assistants
- Data Engineers
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 14 分鐘
Preface
2 個視頻 （總計 14 分鐘）
完成時間為 2 小時
Basic Prediction and Recommendation Metrics
5 個視頻 （總計 57 分鐘）, 1 個閱讀材料, 1 個測驗
5 個視頻
Prediction Accuracy Metrics12分鐘
Decision Support Metrics16分鐘
Rank-Aware Top-N Metrics18分鐘
Assignment Intro Video2分鐘
1 個閱讀材料
Metric Computation Assignment Instructions10分鐘
1 個練習
Basic Prediction and Recommendation Metrics Assignment42分鐘
週
2完成時間為 2 小時
Advanced Metrics and Offline Evaluation
6 個視頻 （總計 76 分鐘）, 1 個閱讀材料, 2 個測驗
6 個視頻
Additional Item and List-Based Metrics18分鐘
Experimental Protocols13分鐘
Unary Data Evaluation11分鐘
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12分鐘
Programming Assignment Introduction8分鐘
1 個閱讀材料
Evaluating Recommenders10分鐘
2 個練習
Offline Evaluation and Metrics Quiz22分鐘
Programming Assignment Quiz28分鐘
週
3完成時間為 1 小時
Online Evaluation
4 個視頻 （總計 66 分鐘）, 1 個測驗
4 個視頻
Usage Logs and Analysis10分鐘
A/B Studies (Field Experiments)11分鐘
User-Centered Evaluation (Interview with Bart Knijnenburg)25分鐘
1 個練習
Online Evaluation Quiz8分鐘
週
4完成時間為 1 小時
Evaluation Design
3 個視頻 （總計 31 分鐘）, 2 個閱讀材料, 1 個測驗
2 個閱讀材料
Intro to Assignment: Evaluation Design Cases10分鐘
Quiz Debrief10分鐘
1 個練習
Assignment: Evaluation Design Cases12分鐘
4.3
23 個審閱來自Recommender Systems: Evaluation and Metrics的熱門評論
wonderful!!! They teach a lot what I did not expect!
講師
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
關於 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.
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.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
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