Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors

Abstract

This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications.

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

“…Another modality showing promise in group detection is WiFi information, where mobility trajectories of users are estimated from the access point (AP) connection events across time [31] and recursively approximating the distance between two users. Shen et al proposed techniques using matrix factorization from the users' similarity matrix of raw WiFi logs with Bluetooth RSS data to detect groups [55,56].…”