Modeling characteristics of location from user photos
In the past decade, location-based services have grown through geo-tagging and place-tagging. Proliferation of GPS-enabled mobile devices further enabled exponential growth in geotagged user content. On the other hand, location-based applications harness the abundance of geo-tagged content to further improve user experience and more relevant localized content. We show in this paper that geo-tagged content can vary significantly based on whether they are captured by a local versus a tourist to the location. Using photos shared by online users, we also show how we can learn unique characteristics about a given location. We finally discuss an effective metric to rank the most representative photos for a given location by combining visual contents and their social engagement potential. Copyright is held by the owner/author(s).
|Keywords||Content ranking, Geo-tagged photos, Information theory, Location-aware|
|Stakeholder||Facebook Inc., San Francisco, CA, USA, Futurewei Technologies, Inc., San Francisco, CA, USA|
|Conference||ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces|
Kumar, V, Bakhshi, S, Kennedy, L, & Shamma, D.A. (2017). Modeling characteristics of location from user photos. In HUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017 (pp. 1–6). doi:10.1145/3039677.3039683