TY - GEN
T1 - Mining user similarity based on location history
AU - Li, Quannan
AU - Zheng, Yu
AU - Xie, Xing
AU - Chen, Yukun
AU - Liu, Wenyu
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2008
Y1 - 2008
N2 - The pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enable people to conveniently log the location histories they visited with spatio-temporal data. The increasing availability of large amounts of spatio-temporal data pertaining to an individual's trajectories has given rise to a variety of geographic information systems, and also brings us opportunities and challenges to automatically discover valuable knowledge from these trajectories. In this paper, we move towards this direction and aim to geographically mine the similarity between users based on their location histories. Such user similarity is significant to individuals, communities and businesses by helping them effectively retrieve the information with high relevance. A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users. In this framework, we take into account both the sequence property of people's movement behaviors and the hierarchy property of geographic spaces. We evaluate this framework using the GPS data collected by 65 volunteers over a period of 6 months in the real world. As a result, HGSM outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures. © 2008 ACM.
AB - The pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enable people to conveniently log the location histories they visited with spatio-temporal data. The increasing availability of large amounts of spatio-temporal data pertaining to an individual's trajectories has given rise to a variety of geographic information systems, and also brings us opportunities and challenges to automatically discover valuable knowledge from these trajectories. In this paper, we move towards this direction and aim to geographically mine the similarity between users based on their location histories. Such user similarity is significant to individuals, communities and businesses by helping them effectively retrieve the information with high relevance. A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users. In this framework, we take into account both the sequence property of people's movement behaviors and the hierarchy property of geographic spaces. We evaluate this framework using the GPS data collected by 65 volunteers over a period of 6 months in the real world. As a result, HGSM outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures. © 2008 ACM.
KW - GPS logs
KW - Similar sequence matching
KW - Spatio-temporal data mining
KW - User similarity
UR - https://www.scopus.com/pages/publications/70449715854
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-70449715854&origin=recordpage
U2 - 10.1145/1463434.1463477
DO - 10.1145/1463434.1463477
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781605583235
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 298
EP - 307
BT - Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
T2 - 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
Y2 - 5 November 2008 through 7 November 2008
ER -