In personalized machine learning, the aim of personalization is to train a model that caters to a specific individual or group of individuals by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective computing and present the first survey of existing approaches for personalization in affective computing. Our review spans training techniques and objectives towards the personalization of affective computing models across various interaction modes and contexts. We develop a taxonomy that clusters existing approaches into Data-level and Model-level approaches. Across the Data-Level and Model-Level broad categories, we group existing approaches into seven sub-categories: (1) User-Specific Models, (2) Group-Specific Models, (3) Weighting-Based Approaches, (4) Feature Augmentation, (5) Generative-Based Models which fall into the Data-Level approaches, (6) Fine-Tuning Approaches, and (7) Multitask Learning Approaches falling under the model-level approaches. We provide a problem formulation for personalized affective computing, and to each of the identified sub-categories. Additionally, we provide a statistical analysis of the surveyed literature, analyzing the prevalence of different affective computing tasks, interaction modes (i.e. Human-Computer Interaction (HCI), Human-Human interaction (HHI), Human-Robot Interaction (HRI)), interaction contexts (e.g. educative, social, gaming, etc.), and the level of personalization among the surveyed works. Based on our analysis, we provide a road-map for researchers interested in exploring this direction.

翻译：在个性化机器学习中，个性化的目标是通过优化一个或多个性能指标并遵循特定约束，来训练一个服务于特定个体或群体的模型。本文讨论了情感计算中对个性化的需求，并首次对情感计算中现有的个性化方法进行了综述。我们的综述涵盖了跨多种交互模式和情境、旨在实现情感计算模型个性化的训练技术与目标。我们构建了一个分类法，将现有方法聚类为数据层面和模型层面的方法。在数据层面和模型层面这两大类别下，我们将现有方法归纳为七个子类别：(1) 用户特定模型，(2) 群体特定模型，(3) 基于权重的方法，(4) 特征增强，(5) 基于生成式模型的方法（属于数据层面方法），(6) 微调方法，以及(7) 多任务学习方法（属于模型层面方法）。我们为个性化情感计算以及每个已识别的子类别提供了问题定义。此外，我们对所综述的文献进行了统计分析，分析了不同情感计算任务、交互模式（即人机交互、人人交互、人机交互）、交互情境（例如教育、社交、游戏等）以及所综述工作中个性化程度的普遍性。基于我们的分析，我们为有意探索此方向的研究者提供了路线图。