Finding Similar Users Using Category-Based Location History

In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls  restaurants  cinemas. Then, we measure the similarity between different users’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].

Finding Similar Users Using Category-Based Location History-4pages.pdf
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In  ACM SIGSPATIAL GIS

Publisher  Association for Computing Machinery, Inc.

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TypeInproceedings

Previous Versions

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.

Quannan Li, Yu Zheng, Xing Xie, and Wei-Ying Ma. Mining user similarity based on location history, Association for Computing Machinery, Inc., 4 November 2008.

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