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

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

Research output: Contribution to journalConference articlepeer-review

57 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2159-2165
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
Publication statusPublished - 2007
Externally publishedYes
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: 2007 Jan 62007 Jan 12

ASJC Scopus subject areas

  • Artificial Intelligence


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