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.
Interview with Yehuda Koren
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来自 University of Minnesota 的课程
Matrix Factorization and Advanced Techniques
University of Minnesota
课程 4（共 5 门，Specialization Recommender Systems）
从本节课中
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (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 the honors track in two weeks unless you start the assignments during the first week.
与讲师见面
Michael D. EkstrandAssistant Professor
Dept. of Computer Science, Boise State University
Joseph A KonstanDistinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
