Inferring Long-term User Properties Based on Users' Location History
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2159-2164.
Abstract
Recent development of location technologies enables us to obtain the location history of users. paper proposes a new method to infer users’ longterm properties from their respective location histories. Counting the instances of sensor detection every user, we can obtain a sensor-user matrix. After generating features from the matrix, a machine learning approach is taken to automatically users into different categories for each user property. Inspired by information retrieval research, problem to infer user properties is reduced 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.
@inproceedings{ijcai2007p348,
title = {Inferring Long-term User Properties Based on Users' Location History},
author = {Matsuo, Yutaka and Okazaki, Naoaki and Izumi, Kiyoshi and Nakamura, Yoshiyuki and Nishimura, Takuichi and Hasida, Kôiti and Nakashima, Hideyuki},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2159--2164},
year = {2007},
url = {https://ijcai.org/proceedings/2007/348},
}
TY - CPAPER TI - Inferring Long-term User Properties Based on Users' Location History AU - Matsuo, Yutaka AU - Okazaki, Naoaki AU - Izumi, Kiyoshi AU - Nakamura, Yoshiyuki AU - Nishimura, Takuichi AU - Hasida, Kôiti AU - Nakashima, Hideyuki T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2159 EP - 2164 UR - https://ijcai.org/proceedings/2007/348 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
