通过过程图上的潜在检索实现推荐的完整后缀预测

中文摘要 AI 辅助

完整后缀预测在序贯决策场景中具有挑战性，因为相同的前缀可能兼容多个合理的后缀。我们提出了一种基于图的度量学习框架，将完整后缀预测重新表述为过程图上的潜在检索。前缀和后缀被表示为有向属性图，并通过边条件图神经网络进行编码，从而能够联合建模事件级活动和转移级持续时间。前缀表示通过一个预测器投影到潜在后缀空间，该预测器使用联合重建和对比目标进行训练，并利用过程感知的困难负样本进行强化。为了稳定学习到的检索几何结构，我们对编码器和预测器应用谱归一化以强制执行Lipschitz约束。在两个真实过程数据集上的实验表明，所提出的框架在几乎所有评估标准上取得了最佳整体结果。它通过归一化Damerau-Levenshtein距离衡量的语义后缀准确性得到提升，通过Recall@1、Recall@5和MRR@5展现出强大的检索质量，并根据平均绝对误差保持了时间合理性。这些结果表明，在结构和KPI相关约束下，基于图的潜在检索是面向推荐的过程监控中序贯后缀预测的有效替代方案。

英文摘要

Complete suffix prediction is challenging in sequential decision settings, where the same prefix can remain compatible with several plausible suffixes. We propose a graphbased metric-learning framework that reformulates complete suffix prediction as latent retrieval over process graphs. Prefixes and suffixes are represented as directed attributed graphs and encoded by edge-conditioned graph neural networks, allowing event-level activities and transition-level durations to be modelled jointly. Prefix representations are projected into the latent suffix space through a predictor trained with a joint reconstruction and contrastive objective strengthened using process-aware hard negatives. To stabilise the learned retrieval geometry, spectral normalisation, and retrieval robustness, spectral normalisation is applied to enforce a Lipschitz constraint on both encoders and predictor. Experiments on two real-life process datasets demonstrate that the proposed framework achieves the best overall results across nearly all evaluated criteria. It improves semantic suffix accuracy measured by normalized Damerau-Levenshtein distance, yields strong retrieval quality through Recall@1, Recall@5, and MRR@5, and maintains temporal plausibility according to Mean Absolute Error. These results show that graph-based latent retrieval is an effective alternative to sequential suffix prediction for recommendation-oriented process monitoring under structural and KPI-related constraints.

发表机构

• LIST3N Lab, AI Lab University of Technology of Troyes (UTT), QAD（特鲁瓦技术大学 LIST3N 实验室、AI 实验室，QAD）
• Arènes (UMR CNRS 605) Ecole des hautes études en santé publique (EHESP)（公共卫生高等学院 Arènes 研究中心（CNRS 605））
• AI Lab QAD（QAD AI 实验室）

机构由 AI 辅助整理，请以论文原文为准。