标题： 基于累积分布函数的一般时间点过程 显示英文标题

标题： Cumulative Distribution Function based General Temporal Point Processes

摘要： 时间点过程（TPPs）在建模跨不同领域的事件序列中起着关键作用，包括社交网络和电子商务，并对推荐系统和信息检索策略的进步做出了重要贡献。通过分析用户交互和交易等事件，TPPs提供了关于行为模式的有价值见解，有助于预测未来趋势。然而，由于这些模式的复杂性，准确预测未来事件仍然是一个严峻的挑战。将神经网络与TPPs相结合，推动了先进深度TPP模型的发展。尽管这些模型在处理复杂和非线性时间数据方面表现出色，但在建模强度函数时遇到限制，在积分计算中面临计算复杂性，并且难以有效捕捉长程时间依赖关系。在本研究中，我们引入了CuFun模型，这是一种围绕累积分布函数（CDF）的新颖TPP方法。CuFun通过独特地使用单调神经网络进行CDF表示，利用过去事件作为缩放因子而脱颖而出。这一创新显著增强了模型在各种数据场景中的适应性和精度。我们的方法解决了传统TPP建模中的几个关键问题：它简化了对数似然计算，扩展了适用性，超越了预定义密度函数形式，并能够有效地捕捉长程时间模式。我们的贡献包括引入一种基于CDF的开创性TPP模型，开发了一种将过去事件信息纳入未来事件预测的方法，并通过在合成和真实世界数据集上的广泛实验验证了CuFun的有效性。 显示英文摘要

摘要： Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrieval strategies. Through the analysis of events such as user interactions and transactions, TPPs offer valuable insights into behavioral patterns, facilitating the prediction of future trends. However, accurately forecasting future events remains a formidable challenge due to the intricate nature of these patterns. The integration of Neural Networks with TPPs has ushered in the development of advanced deep TPP models. While these models excel at processing complex and nonlinear temporal data, they encounter limitations in modeling intensity functions, grapple with computational complexities in integral computations, and struggle to capture long-range temporal dependencies effectively. In this study, we introduce the CuFun model, representing a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF). CuFun stands out by uniquely employing a monotonic neural network for CDF representation, utilizing past events as a scaling factor. This innovation significantly bolsters the model's adaptability and precision across a wide range of data scenarios. Our approach addresses several critical issues inherent in traditional TPP modeling: it simplifies log-likelihood calculations, extends applicability beyond predefined density function forms, and adeptly captures long-range temporal patterns. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.