Analyzing <scp>W</scp>eb behavior in indoor retail spaces

Ren, Yongli; Tomko, Martin; Salim, Flora D.; Ong, Kevin; Sanderson, Mark

doi:10.1002/asi.23587

Cited by 20 publications

(17 citation statements)

References 39 publications

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“…In addition, sequential or continuous behaviours of the whole trajectory in a session is not yet incorporated in the CPS model of this paper. From our earlier paper, we have observed repeating and habitual behaviours of returning visitors are observed across the history [28], which could be used for continuous trajetory prediction [30] for example, or intelligent notification, shopping assistant, or recommendation [26] purposes.…”

Section: Discussionmentioning

confidence: 99%

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

Kaur

Salim

Ren

et al. 2020

ACM Trans. Sen. Netw.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Kaur

Salim

Ren

et al. 2020

ACM Trans. Sen. Netw.

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“…The regions were then manually rectified to better align with the frontages of physical stores in the mall (see [39] for details). Shop frontages are the main determinants of context as the WiFi network studied is designed to only cover common spaces of the mall.…”

Section: Association Logmentioning

confidence: 99%

“…We do not consider the social status here, because as pointed out by [39], the accompanying status of the mall visitors is largely influenced by whether the accompanying peer has a mobile device that is registered to the mall's free WiFi system.…”

Section: Logs Vs Questionnairementioning

confidence: 99%

Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces

et al. 2018

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Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understanding of customer Cyber (online), Physical, and (some aspects of ) Social (CPS) behaviour, at the conjunction of corresponding CPS spaces. We combine the results of a traditional questionnaire with large-scale WiFi access logs, which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources. We find (1) strong correlations between users' demographics and their CPS behaviors; (2) log-recorded cyber-physical behavior reflects well data captured in the corresponding questionnaire; (3) different CPS behaviors contribute differently to the predictability of demographic attributes; and (4) the predictability of user demographics from logs is comparable to questionnaire-based data. As such, our study provides strong support for demographic studies based on large-scale logs data capture.

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“…Current wireless communication infrastructure and technology in managed environments such as university campuses, shopping malls and downtown areas allows the collection of tracking data of its users 14,15 . The operators of the infrastructure will use this data for the optimization of their services, but other parties have an interest in this data as well, and some of the possible uses might be accepted by the users as useful, legitimate, or desirable.…”

Section: Introductionmentioning

confidence: 99%

Paths to Social Licence for Tracking-data Analytics

White¹,

Dennis²,

Bell³

et al. 2020

Preprint

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While tracking-data analytics can be a goldmine for institutions and companies, the inherent privacy concerns also form a legal, ethical and social minefield. We present a study that seeks to understand the extent and circumstances under which tracking-data analytics is undertaken with social licence — that is, with broad community acceptance beyond formal compliance with legal requirements. Taking a University campus environment as a case, we enquire about the social licence for Wi-Fi-based tracking-data analytics. Staff and student participants answered a questionnaire presenting hypothetical scenarios involving Wi-Fi tracking for university research and services. Our results present a Bayesian logistic mixed-effects regression of acceptability judgements as a function of participant ratings on 11 privacy dimensions. Results show widespread acceptance of tracking-data analytics on campus and suggest that trust, individual benefit, data sensitivity, risk of harm and institutional respect for privacy are the most predictive factors determining this acceptance judgement.

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Analyzing Web behavior in indoor retail spaces

Cited by 20 publications

References 39 publications

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

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

Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces

Paths to Social Licence for Tracking-data Analytics

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