当前位置: X-MOL 学术ACM Trans. Sens. Netw. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
ACM Transactions on Sensor Networks ( IF 4.1 ) Pub Date : 2020-07-07 , DOI: 10.1145/3393692
Manpreet Kaur 1 , Flora D. Salim 2 , Yongli Ren 2 , Jeffrey Chan 2 , Martin Tomko 3 , Mark Sanderson 2
Affiliation  

This article 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,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.

中文翻译:

网络活动和物理环境的联合建模以改进访客行为的预测

本文通过利用匿名(选择加入)Wi-Fi 关联和浏览商场运营商记录的日志,调查大型室内购物中心用户的网络物理行为。我们的分析表明,许多用户在他们的网络活动和他们的物理环境之间表现出高度相关性。为了找到这种相关性,提出一种机制,用来自 DBPedia 概念的丰富分类信息对物理空间进行语义标记,并计算表示用户活动与商场上下文的上下文相似度。我们展示了网络物理上下文相似性在两种情况下的应用:用户访问意图分类和未来位置预测。实验结果表明,利用上下文相似性显着提高了此类应用程序的准确性。
更新日期:2020-07-07
down
wechat
bug