Abstract
With the rapid development of GPS-enabled mobile devices, people like to publish online data with geographic information. The traditional online friend recommendation methods usually focus on the shared interests, topics or social network links, but neglect the more and more important geographic information. In this paper, we focus on users’ geographic trajectories that consisting of a series of positions in time order. We reduce the length of each trajectory by clustering the points and normalize every trajectory according to its positions and time in the trajectory. The similarity between trajectories is computed based on the distance of each corresponding point pair in the respective trajectory and the trajectories’ trends. The potential online friends are recommended based on the trajectory similarity and social network structures. Extensive experiment results have validated the feasibility and effectiveness of our proposed approach.
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Feng, S., Huang, D., Song, K., Wang, D. (2013). Online Friends Recommendation Based on Geographic Trajectories and Social Relations. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_28
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DOI: https://doi.org/10.1007/978-3-642-53914-5_28
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