TISIS：用于相似性搜索的轨迹索引
TISIS : Trajectory Indexing for SImilarity Search
AI总结：
本文提出TISIS及其变体TISIS*，通过轨迹索引和POI嵌入实现高效的相似轨迹检索，显著优于基于LCSS算法的基线方法。
摘要：
社交媒体平台使用户能够分享多种类型的信息，包括捕获其移动模式的地理位置数据。此类地理位置数据可被利用来重建用户访问过的兴趣点（POI）的轨迹。在众多应用中，一个关键需求是能够测量此类轨迹之间的相似性，因为这有助于检索与给定参考轨迹相似的轨迹。这是我们工作的主要重点。现有方法主要依赖于对每个候选轨迹应用相似性函数来识别那些足够相似的轨迹。然而，在处理大规模数据集时，这种方法在计算上变得非常昂贵。为了缓解这一挑战，我们提出了TISIS，这是一种高效的方法，它使用轨迹索引来快速找到以相同顺序共享共同POI的相似轨迹。此外，为了考虑轨迹中的POI可能不完全匹配但在上下文上相似的场景，我们引入了TISIS*，这是TISIS的一个变体，它结合了POI嵌入。此扩展通过考虑POI之间的语义相似性，超越了单纯的精确匹配，从而允许更全面地检索相似轨迹。广泛的实验评估表明，所提出的方法显著优于基于著名的最长公共子序列（LCSS）算法的基线方法，在各种真实世界数据集上产生了显著的性能提升。
英文摘要：
Social media platforms enable users to share diverse types of information, including geolocation data that captures their movement patterns. Such geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of Interest (POIs). A key requirement in numerous applications is the ability to measure the similarity between such trajectories, as this facilitates the retrieval of trajectories that are similar to a given reference trajectory. This is the main focus of our work. Existing methods predominantly rely on applying a similarity function to each candidate trajectory to identify those that are sufficiently similar. However, this approach becomes computationally expensive when dealing with large-scale datasets. To mitigate this challenge, we propose TISIS, an efficient method that uses trajectory indexing to quickly find similar trajectories that share common POIs in the same order. Furthermore, to account for scenarios where POIs in trajectories may not exactly match but are contextually similar, we introduce TISIS*, a variant of TISIS that incorporates POI embeddings. This extension allows for more comprehensive retrieval of similar trajectories by considering semantic similarities between POIs, beyond mere exact matches. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms a baseline method based on the well-known Longest Common SubSequence (LCSS) algorithm, yielding substantial performance improvements across various real-world datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
