  In computing, the aim of personalization is to train a model that caters to a
specific individual or group of people by optimizing one or more performance
metrics and adhering to specific constraints. In this paper, we discuss the
need for personalization in affective and personality computing (hereinafter
referred to as affective computing). We present a survey of state-of-the-art
approaches for personalization in affective computing. Our review spans
training techniques and objectives towards the personalization of affective
computing models. We group existing approaches into seven categories: (1)
Target-specific Models, (2) Group-specific Models, (3) Weighting-based
Approaches, (4) Fine-tuning Approaches, (5) Multitask Learning, (6)
Generative-based Models, and (7) Feature Augmentation. Additionally, we provide
a statistical meta-analysis of the surveyed literature, analyzing the
prevalence of different affective computing tasks, interaction modes,
interaction contexts, and the level of personalization among the surveyed
works. Based on that, we provide a road-map for those who are interested in
exploring this direction.
