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计算机系统应用:2018,27(12):227-233
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基于地理标签的LBSN链接预测模型
王勇1, 王超2, 程凯3
(1.铜陵有色金属集团股份有限公司金冠铜业分公司, 铜陵 244000;2.金诚信矿业管理股份有限公司, 北京 100044;3.北京宸控科技有限公司, 北京 102200)
LBSN Link Prediction Model Based on Geographic Tag
WANG Yong1, WANG Chao2, CHENG Kai3
(1.Gold Crown Copper Branch, Tongling Nonferrous Metal Group Co. Ltd., Tongling 244000, China;2.JCHX Mining Management Co. Ltd., Beijing 100044, China;3.Beijing KingKong Science & Technology Co. Ltd., Beijing 102200, China)
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投稿时间:2018-04-22    修订日期:2018-05-14
中文摘要: 为更深入挖掘用户位置信息,本文从位置语义相似性角度挖掘用户特征.利用LDA算法对用户签到信息进行位置主题建模,采用Gibbs采样算法计算LDA模型中的分布函数,并根据这些分布提出了基于签到地点语义的用户相似性特征向量.利用有监督的机器学习算法,综合LBSN的网络结构信息、签到地点信息、地点语义信息得到多维相似性特征向量来进行链接预测.在Gowalla数据集上的实验结果表明,相较于传统的链接预测算法,将基于签到信息的多个相似性特征作为辅助信息的链接预测算法显著提高了LBSN链接预测的性能.
Abstract:In order to find out more location information from users, the user feature is dug out from location semantic similarity. The location topic model is built for user sign-in information by using LDA algorithm, and distribution functions can be calculated by the Gibbs sampling algorithm. The user similarity feature vector based on sign-in location semantic is put forward by these distribution functions. Then, the supervised machine learning algorithm is put forward to make link prediction by multi-dimensional similarity feature vector from fusing LBSN network structure information, sign-in location information and location semantic similarity. The experiments result on Gowalla databases shows that the link prediction algorithm using more similarity feature as subsidiary information can improve performance of LBSN link prediction significantly comparing with the traditional algorithm.
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王勇,王超,程凯.基于地理标签的LBSN链接预测模型.计算机系统应用,2018,27(12):227-233
WANG Yong,WANG Chao,CHENG Kai.LBSN Link Prediction Model Based on Geographic Tag.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):227-233

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