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Geo-social recommendations based on incremental tensor reduction and local path traversal

Symeniodis, Panagiotis
Papadimitriou, Alexis
Manolopoulos, Yannis
Karagöz, Pınar
Toroslu, İsmail Hakkı
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 Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent 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. 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 two real data sets and measure its effectiveness through recall/precision.