Location Prediction: A Temporal-Spatial Bayesian Model
文献类型：期刊论文
| 作者 | Jia, Yantao; Wang, Yuanzhuo; Jin, Xiaolong; Cheng, Xueqi |
| 刊名 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
 |
| 出版日期 | 2016-04-01 |
| 卷号 | 7期号:3页码:25 |
| 关键词 | Design Algorithms Performance Location prediction influential friends dynamic selection temporal-spatial evolution dynamic Bayesian network |
| ISSN号 | 2157-6904 |
| DOI | 10.1145/2816824 |
| 英文摘要 | In social networks, predicting a user's location mainly depends on those of his/her friends, where the key lies in how to select his/her most influential friends. In this article, we analyze the theoretically maximal accuracy of location prediction based on friends' locations and compare it with the practical accuracy obtained by the state-of-the-art location prediction methods. Upon observing a big gap between the theoretical and practical accuracy, we propose a new strategy for selecting influential friends in order to improve the practical location prediction accuracy. Specifically, several features are defined to measure the influence of the friends on a user's location, based on which we put forth a sequential random-walk-with-restart procedure to rank the friends of the user in terms of their influence. By dynamically selecting the top N most influential friends of the user per time slice, we develop a temporal-spatial Bayesian model to characterize the dynamics of friends' influence for location prediction. Finally, extensive experimental results on datasets of real social networks demonstrate that the proposed influential friend selection method and temporal-spatial Bayesian model can significantly improve the accuracy of location prediction. |
| 资助项目 | National Program (973 Program) on Key Basic Research Project of China[2013CB329602] ; National Program (973 Program) on Key Basic Research Project of China[2012CB316303] ; National Natural Science Foundation of China[61173008] ; National Natural Science Foundation of China[61100175] ; National Natural Science Foundation of China[61232010] ; National Natural Science Foundation of China[61303244] ; National Natural Science Foundation of China[61402442] ; Beijing Nova Program[Z121101002512063] ; Beijing Natural Science Foundation[4154086] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000373911200006 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8471]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Jia, Yantao; Wang, Yuanzhuo; Jin, Xiaolong; Cheng, Xueqi |
| 作者单位 | Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jia, Yantao,Wang, Yuanzhuo,Jin, Xiaolong,et al. Location Prediction: A Temporal-Spatial Bayesian Model[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2016,7(3):25. |
| APA | Jia, Yantao,Wang, Yuanzhuo,Jin, Xiaolong,&Cheng, Xueqi.(2016).Location Prediction: A Temporal-Spatial Bayesian Model.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,7(3),25. |
| MLA | Jia, Yantao,et al."Location Prediction: A Temporal-Spatial Bayesian Model".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 7.3(2016):25. |
入库方式： OAI收割
来源：计算技术研究所
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