用近邻引导学习轨迹相似性，准确率提升22%
K Nearest Neighbor-Guided Trajectory Similarity Learning
- 引入子视图建模，捕捉多粒度轨迹模式
- 基于最近邻的损失函数，提升相对排序精度
- 适合轨迹分析、时空数据挖掘等场景
轨迹相似性是众多时空数据挖掘应用的基础。近期研究提出深度学习模型以逼近传统轨迹相似性度量，具备训练后推理速度快的优势。然而，由于轨迹粒度建模困难及训练数据中相似性信号利用不足，相似性逼近精度仍存挑战。为此，本文提出TSMini，一种高效轨迹相似性模型，包含子视图建模机制，可学习多粒度轨迹模式；以及基于k近邻的损失函数，引导模型不仅学习轨迹间的绝对相似值，还学习其相对相似排名。两项创新协同，实现高精度轨迹相似性逼近。实验表明，TSMini在学习轨迹相似性度量时，平均准确率相较现有最优模型提升22%。
原文摘要 · Abstract (English)
Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in the training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism capable of learning multi-granularity trajectory patterns and a k nearest neighbor-based loss that guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these two innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini can outperform the state-of-the-art models by 22% in accuracy on average when learning trajectory similarity measures.
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