Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification, fairness, and knowledge-guided machine learning, can improve entity-aware modeling.

翻译：个体对外部驱动因素的响应预测在许多学科中至关重要。近年来的机器学习（ML）进展催生了最先进的响应预测模型。由于不同实体（任务）间数据的异质性，基于群体层面构建的模型在诸多个性化预测场景中往往表现欠佳。在个性化预测中，核心目标是融入不同实体的内在特征以提升预测性能。本综述聚焦于机器学习领域在面向实体建模方法上的最新发展。当实体特征可实时获取时，机器学习算法常利用这些特征调控网络。然而在多数现实场景中，这些特征并非现成可得，因此学界提出了多种从数据中推断实体特征的机器学习方法。本综述根据实体特征的可获取性及训练数据规模，系统梳理了当前面向实体建模的研究文献，并着重阐释了不确定性量化、公平性及知识引导机器学习等其他学科的最新创新如何推动面向实体建模的改进。