In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
제공자:
이 강좌에 대하여
학습자 경력 결과
50%
학습자 경력 결과
50%
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
Preface
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
Matrix Factorization (Part 2)
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
검토
MATRIX FACTORIZATION AND ADVANCED TECHNIQUES의 최상위 리뷰
Really enjoyed the course! One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)
Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
The content is really good, but overall the interviews with experts in the field are the best of this course.
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.
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.

자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
Is financial aid available?
강좌를 수료하면 대학 학점을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.