Adaptive city characteristics: How location familiarity changes what is regionally descriptive
Proliferation of GPS-enabled mobile devices has brought a plurality of location-Aware applications leveraging the location characteristics in the shared content, like photos and checkins. While these applications provide contextual and relevant information, they also assume geo-Tagged contents to be representative of the geo-bounded characteristics of location. In this paper, however, we show that the characteristics geo-Tagged contents capture about a location can vary based on the familiarity of user (sharing the content) with the location. Using a large dataset of geo-Tagged photos, we learn descriptive spatial photo characteristics and user temporal-location-familiarity to highlight unique characteristics photos capture of location, which vary significantly if taken by locals versus tourists. We then propose a ranking-Approach to find most representative photos for a given city. A user-based evaluation shows photos are more diverse and characteristic of location compared to other popular baselines while being representative of how locals and tourists would describe the city.
|, , , ,|
|Facebook Inc., Menlo Park, CA, USA, Futurewei Technologies Inc., Santa Clara, CA, USA|
|Conference on User Modeling, Adaptation and Personalization|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Kumar, V, Bakhshi, S, Kennedy, L, & Shamma, D.A. (2017). Adaptive city characteristics: How location familiarity changes what is regionally descriptive. In UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 131–139). doi:10.1145/3079628.3079665