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.
Этот курс входит в специализацию ''Специализация Рекомендательные системы'
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Миннесотский университет
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Программа курса: что вы изучите
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.
Рецензии
- 5 stars53,29 %
- 4 stars33,51 %
- 3 stars7,69 %
- 2 stars4,39 %
- 1 star1,09 %
Лучшие отзывы о курсе MATRIX FACTORIZATION AND ADVANCED TECHNIQUES
Awesome course especially for those doing Ph.D in recommender systems
Interview with Francesco Ricci is very knowledgeable about context aware Recommender System.
It will be great, if we can do honor's track with Python or R
Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
Специализация Рекомендательные системы: общие сведения
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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