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
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중급 단계
완료하는 데 약 23시간 필요
영어
자막: 프랑스어, 포르투갈어 (브라질), 러시아어, 영어, 스페인어
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
36%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
11%
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 23시간 필요
영어
자막: 프랑스어, 포르투갈어 (브라질), 러시아어, 영어, 스페인어
제공자:
미네소타 대학교
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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 4시간 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 4시간 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-Quiz30m
완료하는 데 10시간 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 10시간 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 Rating30m
Assignment #1: Response #2: Top Movies by Count30m
Assignment #1: Response #3: Top Movies by Percent Liking30m
Assignment #1: Response #4: Association with Toy Story30m
Assignment #1: Response #5: Correlation with Toy Story30m
Assignment #1: Response #6: Male-Female Differences in Average Rating30m
Assignment #1: Response #7: Male-Female differences in Liking30m
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.
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