|Nikolaos Lagos, Ioan Calapodescu|
|4th International workshop on Data Analytics solutions for Real-LIfe Applications at the 23rd International Conference on Extending Database Technology (EDBT) Conference, Copenhagen, Denmark, 30 March, 2020|
Point of Interest (POI) categories can facilitate a number of services, such as location-based search and place recommendation. However, such information can be incomplete and/or incorrect, especially in crowdsourcing environments. In the literature, automatic category imputation has been suggested to tackle this problem, showing that contextual information is vital for increasing the quality of such predictions. To this end, users’ check-in data, and most particularly location and time of visit, is often used as the notion of context. In this work, we propose a method that considers culture as a contextual parameter. Contrary to existing methods, our approach does not require access to user data. We illustrate the feasibility of our method by performing experiments on data from Foursquare, a global location-based social network.
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
FunctionalThis website uses functional cookies which are required for the search function to work and to apply for jobs and internships.
AnalyticalOur website uses analytical cookies to make it possible to analyse our website and optimize its usability.
Social mediaOur website places social media cookies to show YouTube and Vimeo videos. Cookies placed by these sites may track your personal data.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.