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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レコメンダシステム の専門講座ミネソタ大学（University of Minnesota）このコースについて
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100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の3コース
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約7時間で修了
英語
字幕：英語
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約7時間で修了
英語
字幕：英語
提供：
ミネソタ大学（University of Minnesota）
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.
シラバス - 本コースの学習内容
14分で修了
Preface
14分で修了
2件のビデオ （合計14分）
2時間で修了
Basic Prediction and Recommendation Metrics
2時間で修了
5件のビデオ （合計57分）, 1 reading, 1 quiz
5件のビデオ
Prediction Accuracy Metrics12 分
Decision Support Metrics16 分
Rank-Aware Top-N Metrics18 分
Assignment Intro Video2 分
1件の学習用教材
Metric Computation Assignment Instructions10 分
1の練習問題
Basic Prediction and Recommendation Metrics Assignment42 分
2時間で修了
Advanced Metrics and Offline Evaluation
2時間で修了
6件のビデオ （合計76分）, 1 reading, 2 quizzes
6件のビデオ
Additional Item and List-Based Metrics18 分
Experimental Protocols13 分
Unary Data Evaluation11 分
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12 分
Programming Assignment Introduction8 分
1件の学習用教材
Evaluating Recommenders10 分
2の練習問題
Offline Evaluation and Metrics Quiz22 分
Programming Assignment Quiz28 分
1時間で修了
Online Evaluation
1時間で修了
4件のビデオ （合計66分）
4件のビデオ
Usage Logs and Analysis10 分
A/B Studies (Field Experiments)11 分
User-Centered Evaluation (Interview with Bart Knijnenburg)25 分
1の練習問題
Online Evaluation Quiz8 分
1時間で修了
Evaluation Design
1時間で修了
3件のビデオ （合計31分）, 2 readings, 1 quiz
2件の学習用教材
Intro to Assignment: Evaluation Design Cases10 分
Quiz Debrief10 分
1の練習問題
Assignment: Evaluation Design Cases12 分
レビュー
RECOMMENDER SYSTEMS: EVALUATION AND METRICS からの人気レビュー
SSにより2017年6月12日
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.
NSにより2019年12月13日
Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable
CSにより2017年7月15日
A lot of very in detail theories and metrics. I wish it could have more hands on experience.
LLにより2017年7月18日
wonderful!!! They teach a lot what I did not expect!
レコメンダシステム専門講座について
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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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
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Is financial aid available?
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