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- LI Wen
- School of Computer Science and Technology, China University of Mining and Technology
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- XIA Shi-xiong
- School of Computer Science and Technology, China University of Mining and Technology
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- LIU Feng
- China National Coal Association
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- ZHANG Lei
- School of Computer Science and Technology, China University of Mining and Technology
抄録
Much research which has shown the usage of social ties could improve the location predictive performance, but as the strength of social ties is varying constantly with time, using the movement data of user's close friends at different times could obtain a better predictive performance. A hybrid Markov location prediction algorithm based on dynamic social ties is presented. The time is divided by the absolute time (week) to mine the long-term changing trend of users' social ties, and then the movements of each week are projected to the workdays and weekends to find the changes of the social circle in different time slices. The segmented friends' movements are compared to the history of the user with our modified cross-sample entropy to discover the individuals who have the relatively high similarity with the user in different time intervals. Finally, the user's historical movement data and his friends' movements at different times which are assigned with the similarity weights are combined to build the hybrid Markov model. The experiments based on a real location-based social network dataset show the hybrid Markov location prediction algorithm could improve 15% predictive accuracy compared with the location prediction algorithms that consider the global strength of social ties.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E98.D (8), 1456-1464, 2015
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390001204377731328
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- NII論文ID
- 130005090398
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可