Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Geo-social information can be utilized for user benefits in many applications. Social interaction in vehicular ad hoc networks (VANETs) is an important source for this type of information. In this paper, we first propose and describe a general architecture of the social VANET system (S-VANET) that supports social interaction through vehicular networks. Then, we present a new carpooling recommendation system that works as S-VANET application. The main objective is to recommend individuals to join their friends during trips or travels. The proposed recommendation system uses check-in history and home location to model users, and utilizes Fast Fourier transform to represent user check-ins and find the similarity between users. The system uses hierarchical clustering with weighted center of mass method to estimate the user home location.