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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来自MATRIX FACTORIZATION AND ADVANCED TECHNIQUES的热门评论
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.
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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.