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
Awesome course especially for those doing Ph.D in recommender systems
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
It will be great, if we can do honor's track with Python or R
Really enjoyed the course! One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)
关于 推荐系统 专项课程
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.