Matrix Factorization and Advanced Techniques

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课程主页: https://www.coursera.org/archive/matrix-factorization

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课程简介

University of Minnesota

课程大纲

This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.

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课程详情

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.

矩阵分解和高级技术:在本课程中,您将学习用于推荐系统的各种矩阵分解和混合机器学习技术。从基本矩阵分解开始,您将了解基于减少用户产品偏好空间的维数来构建推荐系统的直觉和实际细节。然后,您将学习将不同算法的优势结合到强大的混合推荐器中的技术。

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