Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence  

作　　者：Junfeng TIAN Zhengqi HOU 

机构地区：[1]School of Cyber Security and Computer,Hebei University,Baoding 071000,China [2]Hebei Key Laboratory of High Confidence Information Systems,Hebei University,Baoding 071000,China

出　　处：《Chinese Journal of Electronics》2024年第3期708-720,共13页电子学报（英文版）

基　　金：supported by the Natural Science Foundation of Hebei Province (Grant No.F2021201058)。

摘　　要：Most of the current research on user friendship speculation in location-based social networks is based on the co-occurrence characteristics of users,however,statistics find that co-occurrence is not common among all users;meanwhile,most of the existing work focuses on mining more features to improve the accuracy but ignoring the time complexity in practical applications.On this basis,a friendship inference model named ITSIC is proposed based on the similarity of user interest tracks and joint user location co-occurrence.By utilizing MeanShift clustering algorithm,ITSIC clustered and filtered user check-ins and divided the dataset into interesting,abnormal,and noise check-ins.User interest trajectories were constructed from user interest check-in data,which allows ITSIC to work efficiently even for users without co-occurrences.At the same time,by application of clustering,the single-moment multi-interest trajectory was further proposed,which increased the richness of the meaning of the trajectory moment.The extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.

关 键 词：Location social network Interest clustering Multi-interest track Track similarity Friendship prediction 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]