@inproceedings{dfc52b8331524d56ba1403666f24f424,
title = "Towards identifying contextual factors on parking lot decisions",
abstract = "The relevance of contextual factors that adapt in-car recommendations to the driver{\textquoteright}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.",
keywords = "Contextual factors, Decision making, In-car recommendations",
author = "Klaus Goffart and Michael Schermann and Christopher Kohl and J{\"o}rg Prei{\ss}inger and Helmut Krcmar",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 ; Conference date: 07-07-2014 Through 11-07-2014",
year = "2014",
doi = "10.1007/978-3-319-08786-3_28",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "320--325",
editor = "Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben",
booktitle = "User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings",
}