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.
이 강좌는 추천 시스템 전문 분야의 일부입니다.
Introduction to Recommender Systems: Non-Personalized and Content-Based
제공자:
About this Course
최근 조회 18,288회
학습자 경력 결과
60%
이 강좌를 수료한 후 새로운 경력 시작하기
40%
이 강좌를 통해 확실한 경력상 이점 얻기
12%
급여 인상 또는 승진하기
귀하가 습득할 기술
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
학습자 경력 결과
60%
이 강좌를 수료한 후 새로운 경력 시작하기
40%
이 강좌를 통해 확실한 경력상 이점 얻기
12%
급여 인상 또는 승진하기
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 16시간 필요
권장: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
영어
자막: 영어
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 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.
완료하는 데 3시간 필요
9개 동영상 (총 147분), 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
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 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
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 readings, 3 quizzes
2개의 동영상
Honors: Intro to programming assignment10m
3개의 읽기 자료
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1시 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 reading
2개의 동영상
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1개의 읽기 자료
Related Readings10m
검토
4.5
INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED의 최상위 리뷰
BS 제공Feb 12, 2019
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 제공Dec 7, 2017
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 제공Sep 18, 2016
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.
PS 제공Dec 10, 2016
As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.
MQ 제공Mar 7, 2017
Overall, the class is perfect. But if you could supply a sample of honour class when we have finished honour codes, it would be perfect.
DR 제공Oct 8, 2016
More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization
SG 제공Jul 6, 2017
Great course. I have already been able to apply what I have learned to me job. Looking forward to the next one.
AS 제공Aug 15, 2019
The course was a good one with content that's understandable. I can't wait to proceed to the next one
WH 제공May 8, 2018
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
PD 제공Jun 24, 2017
Great, thorough introduction with tracks for both Java programmers and non-programmers.
KR 제공Oct 8, 2017
Well-designed assignments and instructive programming exercises in the honors track.
AR 제공Jun 25, 2017
An excellent in-depth introduction into the concepts around recommendation systems!
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?
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.
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
