ML_推荐系统与降维

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.

转载于:https://www.cnblogs.com/sxbjdl/p/5657691.html