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.
제공자:
About this Course
최근 조회 5,540회
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 9시간 필요
권장: 10 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 14분 필요
Preface
완료하는 데 14분 필요
2개 동영상 (총 14분)
완료하는 데 2시간 필요
Basic Prediction and Recommendation Metrics
완료하는 데 2시간 필요
5개 동영상 (총 57분), 1 reading, 1 quiz
5개의 동영상
Prediction Accuracy Metrics12m
Decision Support Metrics16m
Rank-Aware Top-N Metrics18m
Assignment Intro Video2m
1개의 읽기 자료
Metric Computation Assignment Instructions10m
1개 연습문제
Basic Prediction and Recommendation Metrics Assignment42m
완료하는 데 2시간 필요
Advanced Metrics and Offline Evaluation
완료하는 데 2시간 필요
6개 동영상 (총 76분), 1 reading, 2 quizzes
6개의 동영상
Additional Item and List-Based Metrics18m
Experimental Protocols13m
Unary Data Evaluation11m
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12m
Programming Assignment Introduction8m
1개의 읽기 자료
Evaluating Recommenders10m
2개 연습문제
Offline Evaluation and Metrics Quiz22m
Programming Assignment Quiz28m
완료하는 데 1시간 필요
Online Evaluation
완료하는 데 1시간 필요
4개 동영상 (총 66분)
4개의 동영상
Usage Logs and Analysis10m
A/B Studies (Field Experiments)11m
User-Centered Evaluation (Interview with Bart Knijnenburg)25m
1개 연습문제
Online Evaluation Quiz8m
완료하는 데 1시간 필요
Evaluation Design
완료하는 데 1시간 필요
3개 동영상 (총 31분), 2 readings, 1 quiz
2개의 읽기 자료
Intro to Assignment: Evaluation Design Cases10m
Quiz Debrief10m
1개 연습문제
Assignment: Evaluation Design Cases12m
검토
4.3
RECOMMENDER SYSTEMS: EVALUATION AND METRICS의 최상위 리뷰
SS 제공Jun 12, 2017
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 제공Dec 13, 2019
Wonderful course provide realtime examples of the pros and cons of each approach and metric, very useful and enjoyable
CS 제공Jul 15, 2017
A lot of very in detail theories and metrics. I wish it could have more hands on experience.
LL 제공Jul 18, 2017
wonderful!!! They teach a lot what I did not expect!
제공자:
미네소타 대학교
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.
추천 시스템 전문 분야 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
