A Human Mobility Prediction Scheme By Using A Hierarchical Interest Model

• Item type: SIG Technical Reports(1)
• 公開日: 2017-06-29
• タイトル: A Human Mobility Prediction Scheme By Using A Hierarchical Interest Model
• タイトル / 言語: en
• タイトル: A Human Mobility Prediction Scheme By Using A Hierarchical Interest Model
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: データ分析
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: ソーシャルイノベーション推進研究室
• 著者所属: ソーシャルイノベーション推進研究室
• 著者所属: 京都大学大学院情報学研究科通信情報システム専攻
• 著者所属(英): en
• 著者所属(英): Social Innovation Promotion Lab., National Institute of Information and Communications Technology,
• 著者所属(英): en
• 著者所属(英): Social Innovation Promotion Lab., National Institute of Information and Communications Technology,
• 著者所属(英): en
• 著者所属(英): Communications and Computer Engineering, Graduate School of Informatics, Kyoto University,
• 著者名: 劉, 巍; 荘司, 洋三; 新熊, 亮一
• 著者名(英): Wei, Liu; Yozo, Shoji; Ryoichi, Shinkuma
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We propose a scheme to predict human mobility in this technical report. First, a hierarchical interest model is introduced to organize the semantic category of location in the human mobility logs and to represent the personalized mobility pattems of a single person. Then, by combining the interest models of many different people, a 3-dimensional tensor with the features of person, time, and the semantic category of location is constructed. Tensor factorization is utilized to reveal people's mobility interest on different kinds of locations. Finally, personalized interest models are recovered from the cumulative tensor and are used to predict human mobility in a person - by - person way. Extensive evaluation results based on a large scale dataset of real check-in records have validated that our proposal achieves better recall, precision, and F - Score in human mobility prediction as compared to the state-of-art approach.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We propose a scheme to predict human mobility in this technical report. First, a hierarchical interest model is introduced to organize the semantic category of location in the human mobility logs and to represent the personalized mobility pattems of a single person. Then, by combining the interest models of many different people, a 3-dimensional tensor with the features of person, time, and the semantic category of location is constructed. Tensor factorization is utilized to reveal people's mobility interest on different kinds of locations. Finally, personalized interest models are recovered from the cumulative tensor and are used to predict human mobility in a person - by - person way. Extensive evaluation results based on a large scale dataset of real check-in records have validated that our proposal achieves better recall, precision, and F - Score in human mobility prediction as compared to the state-of-art approach.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10539261
• 書誌情報: 研究報告ドキュメントコミュニケーション（DC）; 巻 2017-DC-105, 号 2, p. 1-6, 発行日 2017-06-29
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8892
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会