English(EN) Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs

基于图的潜在检索框架提高了后缀预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 14:59

研究人员开发了一种新颖的基于图的度量学习框架，用于顺序决策场景中的完整后缀预测。该方法将问题重新表述为过程图上的潜在检索，利用边条件图神经网络来模拟事件级活动和转换持续时间。在真实过程数据集上的实验表明，与现有方法相比，在语义后缀精度、检索质量和时间合理性方面都有显著提高。 AI

影响这项研究引入了一种新颖的顺序预测方法，有望改进推荐系统和过程监控。

排序理由这是一篇研究论文，详细介绍了一种使用图神经网络进行完整后缀预测的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yoann Valero · 2026-09-14 14:59

通过过程图上的潜在检索完成推荐的后缀预测

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 …