Geo-social Recommendations
Alexis Papadimitriou, Panagiotis D. Symeonidis, Yannis Manolopoulos · 2011
Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to “check-in ” at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called GeoSocial Recommender System, where users can get recommendations on friends, locations and activities. In order to provide recommendations, we represent this data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. Also, as more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with a real data set and measure its effectiveness through recall/precision.