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.
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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来自MATRIX FACTORIZATION AND ADVANCED TECHNIQUES的热门评论
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.
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
Interview with Francesco Ricci is very knowledgeable about context aware Recommender System.
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