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Mining user similarity based on location history

Quannan Li, Yu Zheng, Xing Xie, and Wei-Ying Ma


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.


Publication typeInproceedings
Published inACM SIGSPATIAL GIS 2008
PublisherAssociation for Computing Machinery, Inc.

Newer versions

Xiangye Xiao, Yu Zheng, Xing Xie, Qiong Luo, and Wei-Ying Ma. Finding Similar Users Using Category-Based Location History, Association for Computing Machinery, Inc., 1 November 2010.

Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, and Wei-Ying Ma. Recommending friends and locations based on individual location history, ACM Transaction on the Web, Association for Computing Machinery, Inc., February 2011.

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