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.
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MATRIX FACTORIZATION AND ADVANCED TECHNIQUES의 최상위 리뷰
It will be great, if we can do honor's track with Python or R
Interview with Francesco Ricci is very knowledgeable about context aware Recommender System.
Awesome course especially for those doing Ph.D in recommender systems
Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
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.
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