解决事件标记不平衡问题，提升稀有标记预测精度。
Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
- 通过阈值调节标记概率，优化稀有标记预测。
- 先预测标记再预测时间，显著提升稀有事件预测效果。
- 无需数值积分，高效支持时间采样与概率估计。
标记时间点过程（MTPP）广泛用于建模带标记事件流中的事件分布，可预测下一个事件的标记和到达时间。然而，现实应用中事件标记分布高度不平衡，某些标记频繁出现而其他标记罕见，这严重影响了稀有事件的预测性能。为此，本文提出一种阈值方法，通过学习阈值来调整基于标记先验概率归一化的标记概率，以优化标记预测，而非直接根据标记概率进行预测。结合该方法，采用先预测标记后预测时间的策略。特别地，设计了一种新型神经MTPP模型，无需计算昂贵的数值非正则积分，即可实现有效的时间采样和标记概率估计。在多个真实数据集上的实验表明，该方法在预测下一事件的标记和时间方面优于多种基线模型。代码已开源：https://github.com/undes1red/IFNMTPP。
原文摘要 · Abstract (English)
Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with some marks being frequent but others rare. The imbalance poses a significant challenge to the performance of the next event prediction, especially for events of rare marks. To address this issue, we propose a thresholding method, which learns thresholds to tune the mark probability normalized by the mark's prior probability to optimize mark prediction, rather than predicting the mark directly based on the mark probability as in existing studies. In conjunction with this method, we predict the mark first and then the time. In particular, we develop a novel neural MTPP model to support effective time sampling and estimation of mark probability without computationally expensive numerical improper integration. Extensive experiments on real-world datasets demonstrate the superior performance of our solution against various baselines for the next event mark and time prediction. The code is available at https://github.com/undes1red/IFNMTPP.
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