全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2019 

移动互联网中基于用户行为的机会连接预测 Opportunistic link prediction based on user behavior in mobile Internet

Keywords: 移动互联网,用户行为,机会连接,随机森林

Full-Text   Cite this paper   Add to My Lib

Abstract:

为提高用户机会连接预测的准确率,进而提升网络服务性能和效率,通过对我国浙江省某市运营商的真实数据集进行模拟实验,研究了用户行为特征与机会连接的相关性.通过分析相遇用户对和非相遇用户对的热点位置分布,空间相似度分布得出用户移动性特征对用户机会连接行为的影响,并在搭建复杂网络模型的基础上采用随机森林算法,综合考虑了网络结构特征、用户移动性特征、用户上网行为特征,对移动互联网中个体在未来一天中某个时间段是否会发生机会连接进行了预测,结果表明,与传统预测方法相比,加入用户移动性特征、用户上网行为特征后的复杂网络模型具有更高的准确率与召回率,利用此模型可更好地提升网络服务性能和效率

References

[1]  Lu X,Wetter E,Bharti N,et al.Approaching the limit of predictability in human mobility[J].Scientific Reports,2013,3(10):2923.
[2]  Song C,Barabasi A L.Limits of predictability in human mobility[J].Science,2010,327(5968):1018-1021.
[3]  Cheng X L,Xu X L.Location prediction based on sequential mining[J].Industrial Control Computer,2013,26(3):70-72.
[4]  Liben Nowell D,Kleinberg J.The link-prediction problem for social networks[J].Journal of the Association for Information Science&Technology,2007,58(7):1019-1031.
[5]  Bulut E,Szymanski B K.Wifi access point deployment for efficient mobile data offloading[J].Acm Sigmobile Mobile Computing&Communications Review,2013,17(1):71-78.
[6]  Socievole A,De Rango F,Marano S.Link prediction in human contact networks using online social ties[C]//Third International Conference on Cloud and Green Computing,September 30-October 2,2013:305-312.
[7]  王铮,任华,方燕萍.随机森林在运营商大数据补全中的应用[J].电信科学,2016,32(12):7-12.Wang Zheng,Ren Hua,Fang Yanping.Application of random forest in big data completion[J].Telecommunications Science,2016,32(12):7-12.
[8]  An N T,Tu M P.A Gaussian mixture model for mobile location prediction[C]//International Conference on Research,Innovation and Vision for the Future(RIVF),February 12-14,2007:914-919.
[9]  Su X Q,Ni H.Probability-based mobility model for mobile users[J].Microcomputer Information,2011,27(6):1-3.
[10]  Liu Z,Fu J H,Zhao N.Users mobile track prediction method based on mobile communication data[J].Computer Applications and Software,2013,30(2):10-17.
[11]  Noulas A,Scellato S,Lathia N,et al.Mining user mobility features for next place prediction in locationbased services[C]//International Conference on Data Mining,December 10-13,2012:1038-1043.
[12]  Monreale A,Pinelli F,Trasarti R,et al.Where next:A location predictor on trajectory pattern mining[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,June 28-July 1,2009:637-646.
[13]  Sun L,Axhausen K W,Lee D H,et al.Understanding metropolitan patterns of daily encounters[J].Proceedings of the National Academy of Sciences of the United States of America,2013,110(34):13774-13779.
[14]  Kim B G,Zhang Y,Schaar M V D,et al.Dynamic pricing and energy consumption scheduling with reinforcement learning[J].IEEE Transactions on Smart Grid,2016,7(5):2187-2198.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133