Awesome

Memory-based-collaborative-filtering

Contain User-based CF(UBCF),Item-based CF(IBCF) A robust k-nearest neighbors Recommender System use MovieLens dataset in Python

User-based collaborative filter

K=25   RunTime：1s RMSE:0.940611 MAE:0.884748.

Memory-based algorithms are easy to implement and produce reasonable prediction quality. The drawback of memory-based CF is that it doesn’t scale to real-world scenarios and doesn’t address the well-known cold-start problem, that is when new user or new item enters the system.