Collaborative filtering algorithm evaluation for various datasets

Most collaborative filtering(CF) research has focused on doing experiments on single dataset or datasets with the same characteristics.This paper presents an analysis of several typical CF algorithms,the User-based KNN method(with 20 neighborhoods),the item-based method,the item average method,the item user average method,and the Slope One method.These algorithms are evaluated on two types of datasets,Movielens and Book-Crossing,which have different user-item distribution characteristics.The results show for the relatively dense ratings on the Movielens dataset,the Slope One method has the best prediction precision,while on datasets with relatively sparse ratings such as Book-Crossing,the item-based method is the best,while the Slope One method is the worst.Thus,the different CF algorithms give different results on the different datasets,so the CF algorithm should be designed according to the user-item distribution characters.