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

Autor:	Kaur, Manpreet, Salim, Flora D., Ren, Yongli, Chan, Jeffrey, Tomko, Martin, Sanderson, Mark
Rok vydání:	2020
Předmět:	Computer Science - Information Retrieval Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning Computer Science - Social and Information Networks
Zdroj:	ACM Transactions on Sensor Networks, 2020
Druh dokumentu:	Working Paper
DOI:	10.1145/3393692
Popis:	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. Comment: Accepted in ACM Transactions on Sensor Networks, 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.11400 Zobrazit plný text záznamu View this record from Arxiv