Finding similar users using category-based location history
Finding similar users using category-based location history
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DOI：
10.1145/1869790.1869857
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
Xiangye Xiao;Yu Zheng;Qiong Luo;Xing Xie
中科院分区：
文献类型：
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作者：
Xiangye Xiao;Yu Zheng;Qiong Luo;Xing Xie
在本文中，我们的目的是根据用户的GPS轨迹来估计用户之间的相似性。我们的方法首先使用语义位置历史对用户的全球定位系统轨迹进行建模，例如，购物中心→餐厅→电影院。然后，我们使用最大旅行匹配(MTM)算法来度量不同用户的SLH之间的相似度。我们方法的优势在于两个方面。首先，SLH在低级地理位置之外承载了更多用户兴趣的语义含义。其次，我们的方法可以估计地理空间中没有重叠的两个用户之间的相似度，例如居住在不同城市的人。我们基于109个用户在1年内收集的真实GPS数据集对我们的方法进行了评估。结果表明，SLH-MTM的性能优于相关工作[4]。
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].