Culture-aware point-of-interest category completion in a global location-based social network database without access to user data

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.