Friendship Inference Based on Interest Trajectory Similarity and Co-Occurrence

Abstract

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.