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

TL;DR

This paper presents a method to enhance visitor behavior prediction in shopping malls by jointly modeling cyber activities and physical context, using semantic labeling and contextual similarity to improve accuracy.

Contribution

It introduces a novel approach to semantically label physical spaces and compute contextual similarity, improving prediction of user behaviors in cyber-physical environments.

Findings

Contextual similarity improves visit intent classification accuracy.

Cyber-physical modeling enhances future location prediction.

Semantic labeling of physical spaces is effective for behavior analysis.

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.