A novel feature selection strategy for friends recommendation
Rui Feng Ding, Jia Hu Zhu, Yong Zhong Tang, Xueqin Lin, Danyang Xiao, Haoye Dong · 2016
With the social network being widely used, people would like to use the friends recommendation provided by a social websites. There are lots of methods to make the recommendation results more accurately and efficiently. By considering the feature selection strategy in the stage of data preprocessing, we propose a novel friend recommendation system using a classification model, which formulates the recommendation problem. We compare the performance of four classifiers, and draw a conclusion that our proposed method can get higher accuracy.