In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an in-depth review of technical advancements, commonly used datasets, and evaluation metrics across multiple modalities, personalized contexts, and tasks. Moreover, we envision the potential applications of PGen and highlight open challenges and promising directions for future exploration. By bridging PGen research across multiple modalities, this survey serves as a valuable resource for fostering knowledge sharing and interdisciplinary collaboration, ultimately contributing to a more personalized digital landscape.
View on arXiv@article{xu2025_2503.02614,
title={ Personalized Generation In Large Model Era: A Survey },
author={ Yiyan Xu and Jinghao Zhang and Alireza Salemi and Xinting Hu and Wenjie Wang and Fuli Feng and Hamed Zamani and Xiangnan He and Tat-Seng Chua },
journal={arXiv preprint arXiv:2503.02614},
year={ 2025 }
}