文档介绍：第 卷 第 期 吉 林 大 学 学 报 理 学 版
5 8 6 ( .School of Graduate J ilin University Changchun China
(1 , , 1 300 1 2, ;
.College of Engineering Northeastern University Boston USA
2 , , 02 1 1 5 , ;
.College of Com puter Science and Technology J ilin University Changchun China
3 , , 1 300 1 2, )
Abstract
: We combined the traditional two memory-based collaborative-filtering methods and
proposed a data-based personalized mixed recommendation method for GitHub proj method
could not only calculate the similar users dynamically to ensure the personalized recommendation,but
also obtain the recommendation quality comparable to the item-based method with only small scale of
similar the same time,the method solved the data sparsity and cold boot problems of the
original method in the face of GitHub,a data set of users and proj ects of an order of magnitude but
K
with low degree of crossover to some extent by establishing inverse table and using -means
cla