Personalized Generation In Large Model Era: A Survey
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910977216217088 |
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| author | Xu, Yiyan Zhang, Jinghao Salemi, Alireza Hu, Xinting Wang, Wenjie Feng, Fuli Zamani, Hamed He, Xiangnan Chua, Tat-Seng |
| author_facet | Xu, Yiyan Zhang, Jinghao Salemi, Alireza Hu, Xinting Wang, Wenjie Feng, Fuli Zamani, Hamed He, Xiangnan Chua, Tat-Seng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Generation In Large Model Era: A Survey Xu, Yiyan Zhang, Jinghao Salemi, Alireza Hu, Xinting Wang, Wenjie Feng, Fuli Zamani, Hamed He, Xiangnan Chua, Tat-Seng Information Retrieval 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. |
| title | Personalized Generation In Large Model Era: A Survey |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2503.02614 |