毕业设计&课设_协同过滤算法项目：含多种实现，使用数据集，分析优缺点，代码语言为 Python.zip

# Memory-based-collaborative-filtering [](https://travis-ci.org/fuhailin/Memory-based-collaborative-filtering) Contain User-based CF([UBCF](https://github.com/fuhailin/Memory-based-collaborative-filtering/blob/master/User_basedCF.py)),Item-based CF([IBCF](https://github.com/fuhailin/Memory-based-collaborative-filtering/blob/master/Item_basedCF.py)) 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.