Inferring long-term user properties based on users' location history

Yutaka Matsuo, Naoaki Okazaki, Kiyoshi Izumi, Yoshiyuki Nakamura, Takuichi Nishimura, Kôiti Hasida, Hideyuki Nakashima

研究成果: Conference article

51 引用 (Scopus)

抜粋

Recent development of location technologies enables us to obtain the location history of users. This paper proposes a new method to infer users' long-term properties from their respective location histories. Counting the instances of sensor detection for every user, we can obtain a sensor-user matrix. After generating features from the matrix, a machine learning approach is taken to automatically classify users into different categories for each user property. Inspired by information retrieval research, the problem to infer user properties is reduced to a text categorization problem. We compare weightings of several features and also propose sensor weighting. Our algorithms are evaluated using experimental location data in an office environment.

元の言語English
ページ(範囲)2159-2165
ページ数7
ジャーナルIJCAI International Joint Conference on Artificial Intelligence
出版物ステータスPublished - 2007 12 1
イベント20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
継続期間: 2007 1 62007 1 12

ASJC Scopus subject areas

  • Artificial Intelligence

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  • これを引用

    Matsuo, Y., Okazaki, N., Izumi, K., Nakamura, Y., Nishimura, T., Hasida, K., & Nakashima, H. (2007). Inferring long-term user properties based on users' location history. IJCAI International Joint Conference on Artificial Intelligence, 2159-2165.