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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Empfehlungsdienste
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University of Minnesota
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
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.
Bewertungen
- 5 stars53,29Â %
- 4 stars33,51Â %
- 3 stars7,69Â %
- 2 stars4,39Â %
- 1 star1,09Â %
Top-Bewertungen von MATRIX FACTORIZATION AND ADVANCED TECHNIQUES
Really enjoyed the course!
One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)
Awesome course especially for those doing Ph.D in recommender systems
The content is really good, but overall the interviews with experts in the field are the best of this course.
Interview with Francesco Ricci
is very knowledgeable about context aware Recommender System.
Über den Spezialisierung Empfehlungsdienste
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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