Trajectory similarity measurement is a vital and widely used step in many applications, including recommendation systems. In the discipline of similarity measurement, research has mostly been focused on raw trajectories, consisting of location and time-stamp information. Due to the explosion in the use of the internet and location-based social networks, raw trajectories can be easily enriched with semantic information. Nevertheless, few attempts have been made to apply semantic and location information during similarity measurement. In light of this, we present a new similarity measure called the enhanced maximal travel match (EMTM) in this article. The proposed EMTM improves on conventional maximal travel match (MTM) methods by simultaneously considering the location and place category as the most basic semantic information. EMTM first generates a new place category-location hierarchical framework (CLHF) for each trajectory. Subsequently, it identifies MTMs at each layer of hierarchy to explore the similarity between each pair of CLHFs. Finally, the proposed method calculates similarity scores based on the identified MTMs. In experiments on a Foursquare data set, EMTM outperformed the conventional MTM methods by more than 50% in terms of mean average precision. Additionally, EMTM was the most accurate technique among four state-of-the-art methods.

中文翻译：

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轨迹相似度测量：一种增强的最大行程匹配方法

轨迹相似性测量是包括推荐系统在内的许多应用程序中重要且广泛使用的步骤。在相似性测量学科中，研究主要集中在原始轨迹上，包括位置和时间戳信息。由于互联网和基于位置的社交网络的使用激增，原始轨迹可以很容易地用语义信息丰富。然而，很少有人尝试在相似性测量期间应用语义和位置信息。鉴于此，我们在本文中提出了一种称为增强型最大旅行匹配 (EMTM) 的新相似性度量。所提出的 EMTM 通过同时将位置和地点类别作为最基本的语义信息来改进传统的最大旅行匹配 (MTM) 方法。EMTM 首先为每个轨迹生成一个新的地点类别位置层次框架（CLHF）。随后，它在层次结构的每一层识别 MTM，以探索每对 CLHF 之间的相似性。最后，所提出的方法根据识别出的 MTM 计算相似度分数。在 Foursquare 数据集的实验中，EMTM 在平均精度方面比传统的 MTM 方法高出 50% 以上。此外，EMTM 是四种最先进方法中最准确的技术。EMTM 在平均精度方面比传统 MTM 方法高出 50% 以上。此外，EMTM 是四种最先进方法中最准确的技术。EMTM 在平均精度方面比传统 MTM 方法高出 50% 以上。此外，EMTM 是四种最先进方法中最准确的技术。