Observing and Modeling User Behavior on Socio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies

Atzmueller, Martin; Güven, Çiçek; Shayan, Parisa; Masiala, Spyroula; Mackenbach, Rick; Liebregts, Werner

doi:10.1109/transai46475.2019.00013

Cited by 3 publications

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“…This paper is an an adapted and substantially extended revision of [9]. In comparison, to our original work, this paper includes the following improvements:…”

Section: Introductionmentioning

confidence: 99%

Behavioral Link Analytics on Heterogeneous Human Interaction Networks

et al. 2020

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For designing and modeling Artificial Intelligence (AI) systems in the area of human-machine interaction, suitable approaches for user modeling are important in order to both capture user characteristics. Using multimodal data, this can be performed from various perspectives. Specifically, for modeling user interactions in human interaction networks, appropriate approaches for capturing those interactions, as well as to analyze them in order to extract meaningful patterns are important. Specifically, for modeling user behavior for the respective AI systems, we can make use of diverse heterogeneous data sources. This paper investigates face-to-face as well as socio-spatial interaction networks for modeling user interactions from three perspectives: We analyze preferences and perceptions of human social interactions in relation to the interactions observed using wearable sensors, i. e., face-to-face as well as socio-spatial interactions fo the respective actors. For that, we investigate the correspondence of according networks, in order to identify conformance, exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of proximity interactions coupled with self-report questionnaires about preferences and perception of those interactions. The different networks, and according perspectives then provide different options for user modeling and integration into AI systems modeling such user behavior.

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“…This paper is an an adapted and substantially extended revision of [9]. In comparison, to our original work, this paper includes the following improvements:…”

Section: Introductionmentioning

confidence: 99%