Abstract
The relevance of contextual factors that adapt in-car recommendations to the driver’s current situation is not yet fully understood. This paper presents a field study that has been conducted in order to identify relevant contextual factors of in-car parking lot recommender systems. Surprisingly, most contextual factors examined, i.e., weather, luggage, and traffic conditions, did not have a significant effect on the parking lot decision in the conducted field study. Only the urgency of the trip and the willingness to walk have significant effects on the decision outcome. Therefore, automobile manufacturers should focus on understanding the relevance of different contextual factors when developing user models for in-car recommender systems.
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Goffart, K., Schermann, M., Kohl, C., Preißinger, J., Krcmar, H. (2014). Towards Identifying Contextual Factors on Parking Lot Decisions. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_28
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DOI: https://doi.org/10.1007/978-3-319-08786-3_28
Publisher Name: Springer, Cham
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