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.

翻译：时间点过程（TPP）在社交网络和电子商务等不同领域的事件序列建模中扮演着关键角色，并为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，TPP为行为模式提供了宝贵见解，有助于预测未来趋势。然而，由于这些模式的复杂特性，准确预测未来事件仍是一项艰巨挑战。神经网络与TPP的结合催生了先进的深度TPP模型的发展。虽然这些模型在处理复杂非线性时间数据方面表现出色，但它们在强度函数建模中面临局限，在积分计算中遭遇计算复杂性难题，且难以有效捕捉长期时间依赖关系。在本研究中，我们引入CuFun模型，这是一种围绕累积分布函数（CDF）的TPP新方法。CuFun的独特之处在于采用单调神经网络进行CDF表示，并利用过去事件作为缩放因子。这一创新显著增强了模型在各种数据场景下的适应性和精度。我们的方法解决了传统TPP建模固有的几个关键问题：简化了对数似然计算，扩展了超出预定义密度函数形式的适用范围，并巧妙地捕捉了长期时间模式。我们的贡献包括：引入开创性的基于CDF的TPP模型，开发将过去事件信息纳入未来事件预测的方法论，以及通过大量合成和真实世界数据集实验对CuFun有效性的实证验证。