Friendship Understanding by Smartphone-based Interactions: A Cross-space Perspective

Thanks to the growing popularity and functionality, smartphone has become rapidly valuable potential tool for human behavior research, e.g., friendship relationship recognition, friend ship prediction, etc. Until recently, there have been many research efforts to study this issue using the sensed data collected from smartphones. However, almost previous works in finding friendship strength are based on several physical features or a few dimensions, such as using Bluetooth scanning and demographic data to explain friendship. Actually, friendship is complicated and coupled with many factors, such as physical propinquity, social, physical and psychological homophily. So, it is necessary and beneficial to examine it comprehensively, by taking into account all the involved factors. Aiming at closing part of this research gap, in this paper, from cross-space perspectives, we launch a friendship relationship study with smartphone-based sensing paradigm from cyber space, physical mobility, and personality trait homophily. By integrating the involved heterogeneous interactions, we propose a Deep AutoEncoder-based unified framework to predict the strength of friendship connections between users, where the friendship strength is categorized and asymmetrical. We conduct extensive experiments on a practically collected sensing data set, and show the efficiency and effectiveness of our proposed approaches.