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Learning Outcomes: By the end of this course, you will be able to:
-Create a collaborative filtering system. 构建一个协调过滤系统
-Reduce dimensionality of data using SVD, PCA, and random projections. 使用SVD、PCA和随机投影进行降维
-Perform matrix factorization using coordinate descent. 使用坐标下降进行矩阵分解
-Deploy latent factor models as a recommender system.
-Handle the cold start problem using side information. 处理冷启动问题
-Examine a product recommendation application.
-Implement these techniques in Python.
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原文地址：http://www.cnblogs.com/sxbjdl/p/5657691.html
