Abstract
Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could explain 10% to 30% of human movement, especially long-distance travel. Therefore, it is necessary to recognize patterns from human mobility to assist the friend recommendation. However, previous works either modelled friendships and check-in records by simple graphs with only one connection between any two nodes or ignored a large amount of vital spatio-temporal information and semantic information in raw LBSN data. To overcome the limitation of the simple graph commonly seen in previous works, we leverage heterogeneous multigraph to model LBSN data and define various semantic connections between nodes. Against this background, we propose a Heterogeneous Multigraph Contrastive Learning (HMGCL) model to capture spatio-temporal characteristics of human trajectories for user node embedding learning. Extensive experiments show that our method outperforms the state-of-the-art approaches in six real-world city datasets.
Yongkang Li
PhD Student
I am currently a PhD student in IR LAB, the University of Amsterdam, work with Prof. Evangelos Kanoulas. Before that, I got my master degree at Southern University of Science and Technology, Department of Computer Science and Engineering, SUSTech-UTokyo Joint Research Center on Super Smart City Lab, where I am supervised by Prof. Xuan Song in SUSTech and Prof. Zipei Fan at the University of Tokyo. What’s more, I received a B.E. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications in 2020.