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：对外部驱动因素造成的个别实体的反应作出个人化预测,在许多学科中至关重要。最近机器学习(ML)的进展导致新的最新反应预测模型。在人口层面建立的模式往往导致许多个性化预测环境中由于跨实体(任务)数据的异质性而出现次优的性能。在个性化预测中,目标是纳入不同实体的固有特征,以改进预测绩效。在本次调查中,我们侧重于ML社区中这种实体认知模型方法的最新动态。ML算法经常在具备这些实体特征时对网络进行调节。然而,在许多现实世界情景中,这些实体特征并不很容易获得,因此提出了从数据中推断这些特征的不同ML方法。在本次调查中,我们根据这些特征的可得性以及培训数据的数量组织了关于实体认知模型的当前文献。我们强调其他学科的最新创新,例如不确定性的量化、公平性和知识引导的机器学习,能够改进实体的建模。