Geo-Social Temporal Top-k Queries in location-based social networks

With recent advancements in location-acquisition techniques and smart phone devices, social networks such as Foursquare, Facebook and Twitter are acquiring the location dimension while minimizing the gap between physical world and virtual social networking. This in return, has resulted in the generation of geo-tagged data at unprecedented scale and has facilitated users to fully capture and share their geo-locations with timestamps on social media. Typical location-based social media allows users to check-in at a location of interest using smart devices which then is published on social network and this information can be exploited for recommendation. In this paper, we propose a new type of query called Geo-Social Temporal Top-k ($$GSTT:k$$) query, which enriches the semantics of the conventional spatial query by introducing social relevance and temporal component. In addition, we propose three different schemes to answer such a query. Finally, we conduct an exhaustive evaluation of proposed schemes and demonstrate the effectiveness of the proposed approaches.