Behavioral Analysis on Socio-Spatial Interaction Networks concerning User Preferences, Interactions and their Perception

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

doi:10.1145/3345002.3349291

Cited by 2 publications

(1 citation statement)

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“…In contrast, [21] demonstrate that informants are practically able to report relatively accurate interactions, especially regarding dyadic interactions. We have presented preliminary work on analyzing user perceptions and preferences in [11,10]. According to the findings reported in [11], the relation between preferences and observed behavior were also relatively weak, however, for specific subgroups a high level of correspondence can be shown.…”

Section: Interactions: Perception and Preferencesmentioning

confidence: 92%

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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