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Using multi-features to recommend friends on location-based social networks

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Abstract

Location-based social networks (LBSNs) not only offer novel services but also produce more abundant data to help new serves for human. It will help discover latent trajectory, possible friendship and then guide trip, predict next place, recommend friends, and promote sales and so on. In this paper we study two problems for friend recommendation on LBSN: what is the main feature and how to predict friendship? We firstly analyze many factors related with human mobility and social relations; adopt the information gain to measure the contribution of different features to human friendship. Then we extract user social relationship, check-in distance during fixed periods and check-in type as key features. Because the prediction problem could be considered as a classification problem, we choose SVM to predict friendship. At last some experiment results show our algorithm valid to some extent.

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Acknowledgments

Partially supported by the National high Technology Research and Development Program of China (863 Program,2014AA015204), Natural Science Foundation of Shanxi Province of China (Grant No. 2014011022-1), the National Natural Science Foundation of China (Grant No. 61472272), the Open Project Funding of CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences.

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Correspondence to Wang Li.

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Xu-Rui, G., Li, W. & Wei-Li, W. Using multi-features to recommend friends on location-based social networks. Peer-to-Peer Netw. Appl. 10, 1323–1330 (2017). https://doi.org/10.1007/s12083-016-0489-5

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  • DOI: https://doi.org/10.1007/s12083-016-0489-5

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