This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
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추천 시스템 특화 과정미네소타 대학교이 강좌에 대하여
최근 조회 27,668회
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40%
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12%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 20시간 필요
영어
자막: 영어
귀하가 습득할 기술
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
학습자 경력 결과
60%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
40%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
12%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 20시간 필요
영어
자막: 영어
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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 1시간 필요
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
완료하는 데 1시간 필요
2개 동영상 (총 41분), 1 개의 읽기 자료
1개의 읽기 자료
Notes on Course Design and Relationship to Prior Courses10m
완료하는 데 3시간 필요
Introducing Recommender Systems
This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.
완료하는 데 3시간 필요
9개 동영상 (총 147분), 2 개의 읽기 자료, 2 개의 테스트
9개의 동영상
Preferences and Ratings17m
Predictions and Recommendations16m
Taxonomy of Recommenders I27m
Taxonomy of Recommenders II21m
Tour of Amazon.com21m
Recommender Systems: Past, Present and Future16m
Introducing the Honors Track7m
Honors: Setting up the development environment10m
2개의 읽기 자료
About the Honors Track10m
Downloads and Resources10m
2개 연습문제
Closing Quiz: Introducing Recommender Systems20m
Honors Track Pre-Quiz2m
완료하는 데 7시간 필요
Non-Personalized and Stereotype-Based Recommenders
In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.
완료하는 데 7시간 필요
7개 동영상 (총 111분), 5 개의 읽기 자료, 9 개의 테스트
7개의 동영상
Summary Statistics I16m
Summary Statistics II22m
Demographics and Related Approaches13m
Product Association Recommenders19m
Assignment #1 Intro Video14m
Assignment Intro: Programming Non-Personalized Recommenders17m
5개의 읽기 자료
External Readings on Ranking and Scoring10m
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10m
Assignment Intro: Programming Non-Personalized Recommenders10m
LensKit Resources10m
Rating Data Information10m
8개 연습문제
Assignment #1: Response #1: Top Movies by Mean Rating10m
Assignment #1: Response #2: Top Movies by Count10m
Assignment #1: Response #3: Top Movies by Percent Liking10m
Assignment #1: Response #4: Association with Toy Story10m
Assignment #1: Response #5: Correlation with Toy Story10m
Assignment #1: Response #6: Male-Female Differences in Average Rating10m
Assignment #1: Response #7: Male-Female differences in Liking8m
Non-Personalized Recommenders20m
완료하는 데 3시간 필요
Content-Based Filtering -- Part I
The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.
완료하는 데 3시간 필요
8개 동영상 (총 156분)
8개의 동영상
TFIDF and Content Filtering24m
Content-Based Filtering: Deeper Dive26m
Entree Style Recommenders -- Robin Burke Interview13m
Case-Based Reasoning -- Interview with Barry Smyth13m
Dialog-Based Recommenders -- Interview with Pearl Pu21m
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11m
Beyond TFIDF -- Interview with Pasquale Lops21m
완료하는 데 6시간 필요
Content-Based Filtering -- Part II
The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.
완료하는 데 6시간 필요
2개 동영상 (총 26분), 3 개의 읽기 자료, 3 개의 테스트
2개의 동영상
Honors: Intro to programming assignment10m
3개의 읽기 자료
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1시간 20분
Tools for Content-Based Filtering10m
CBF Programming Intro10m
2개 연습문제
Assignment #2 Answer Form20m
Content-Based Filtering20m
완료하는 데 1시간 필요
Course Wrap-up
We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).
완료하는 데 1시간 필요
2개 동영상 (총 45분), 1 개의 읽기 자료
2개의 동영상
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1개의 읽기 자료
Related Readings10m
검토
INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED의 최상위 리뷰
BS 제공2019년 2월 12일
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
DP 제공2017년 12월 7일
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
IP 제공2016년 9월 18일
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
NA 제공2020년 4월 6일
The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization
추천 시스템 특화 과정 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
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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예, Coursera에서는 수업료를 낼 수 없는 학습자를 위해 재정 지원을 제공합니다. 왼쪽에 있는 등록 버튼 아래 재정 지원 링크를 클릭하면 지원할 수 있습니다. 신청서를 작성하라는 메시지가 표시되며 승인되면 알림을 받습니다. 성취 프로젝트를 포함하여 전문 분야의 각 강좌에서 이 단계를 완료해야 합니다. 자세히 알아보기.
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.
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이 강좌는 대학 학점을 제공하지 않지만, 일부 대학에서 선택적으로 강좌 수료증을 학점으로 인정할 수도 있습니다. 자세한 내용은 해당 기관에 문의하세요. Coursera의 온라인 학위 및 Mastertrack™ 수료증은 대학 학점을 취득할 기회를 제공합니다.
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