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.
Informações sobre o curso
Universidade de MinnesotaUniversidade de 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.
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Principais avaliações do 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)
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.
Sobre Programa de cursos integrados Sistemas de recomendação
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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