Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
This work addresses the problem of enhancing behavioral prediction for mall operators and marketers, but it is incremental as it builds on existing cyber-physical integration methods.
The paper tackled the problem of predicting visitor behaviors in a shopping mall by analyzing correlations between cyber activities (Wi-Fi and browsing logs) and physical context, using semantic labeling from DBPedia to compute contextual similarity. The result showed that this approach significantly improved accuracy in tasks like user visit intent classification and future location prediction.
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.