ABSTRACT
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.
- L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In Proceedings of the 19th international conference on World wide web, pages 61--70. ACM, 2010. Google ScholarDigital Library
- J. Bao, Y. Zheng, and M. F. Mokbel. Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pages 199--208. ACM, 2012. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 759--768. ACM, 2010. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. Exploring millions of footprints in location sharing services. ICWSM, 2011, pages 81--88, 2011.Google Scholar
- M. Research. Geolife gps trajectories, Aug. 2012.Google Scholar
- C. Statistics. The largest cities in the world by land area, population and density, Jan. 2007.Google Scholar
Index Terms
- A carpooling recommendation system based on social VANET and geo-social data
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