This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous m...This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.展开更多
Recent advancements in diffusion models have significantly impacted content creation,leading to the emergence of per-sonalized content synthesis(PCS).By utilizing a small set of user-provided examples featuring the sa...Recent advancements in diffusion models have significantly impacted content creation,leading to the emergence of per-sonalized content synthesis(PCS).By utilizing a small set of user-provided examples featuring the same subject,PCS aims to tailor this subject to specific user-defined prompts.Over the past two years,more than 150 methods have been introduced in this area.However,existing surveys primarily focus on text-to-image generation,with few providing up-to-date summaries on PCS.This pa-per provides a comprehensive survey of PCS,introducing the general frameworks of PCS research,which can be categorized into test-time fine-tuning(TTF)and pre-trained adaptation(PTA)approaches.We analyze the strengths,limitations and key tech-niques of these methodologies.Additionally,we explore specialized tasks within the field,such as object,face and style personaliza-tion,while highlighting their unique challenges and innovations.Despite the promising progress,we also discuss ongoing challenges,including overfitting and the trade-off between subject fidelity and text alignment.Through this detailed overview and analysis,we propose future directions to further the development of PCS.展开更多
文摘This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.
基金supported in part by Chinese National Natural Science Foundation Projects,China(Nos.U23B2054,62276254 and 62372314)Beijing Natural Science Foundation,China(No.L221013)+1 种基金InnoHK program,and Hong Kong Research Grants Council through Research Impact Fund,China(No.R1015-23)Open access funding provided by The Hong Kong Polytechnic University,China.
文摘Recent advancements in diffusion models have significantly impacted content creation,leading to the emergence of per-sonalized content synthesis(PCS).By utilizing a small set of user-provided examples featuring the same subject,PCS aims to tailor this subject to specific user-defined prompts.Over the past two years,more than 150 methods have been introduced in this area.However,existing surveys primarily focus on text-to-image generation,with few providing up-to-date summaries on PCS.This pa-per provides a comprehensive survey of PCS,introducing the general frameworks of PCS research,which can be categorized into test-time fine-tuning(TTF)and pre-trained adaptation(PTA)approaches.We analyze the strengths,limitations and key tech-niques of these methodologies.Additionally,we explore specialized tasks within the field,such as object,face and style personaliza-tion,while highlighting their unique challenges and innovations.Despite the promising progress,we also discuss ongoing challenges,including overfitting and the trade-off between subject fidelity and text alignment.Through this detailed overview and analysis,we propose future directions to further the development of PCS.
