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.
After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit.
In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
이 강좌는 추천 시스템 전문 분야의 일부입니다.
Introduction to Recommender Systems: Non-Personalized and Content-Based
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About this Course
4.5
399개의 평가
귀하가 습득할 기술
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 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.
2 videos (Total 41 min), 1 reading
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.
9 videos (Total 147 min), 2 readings, 2 quizzes
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
주
2완료하는 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.
7 videos (Total 111 min), 5 readings, 9 quizzes
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
주
3완료하는 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.
8 videos (Total 156 min)
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
주
4완료하는 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.
2 videos (Total 26 min), 3 readings, 3 quizzes
2개의 동영상
Honors: Intro to programming assignment10m
3개의 읽기 자료
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20m
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).
2 videos (Total 45 min), 1 reading
2개의 동영상
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1개의 읽기 자료
Related Readings10m
4.5
76개의 리뷰75%
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14%
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최상위 리뷰
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.
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).
강사
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
미네소타 대학교 정보
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
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
자주 묻는 질문
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재정 지원을 받을 수 있나요?
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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