NextAds: Towards Next-generation Personalized Video Advertising

With the rapid growth of online video consumption, video advertising has become increasingly dominant in the digital advertising landscape. Yet diverse users and viewing contexts make one-size-fits-all ad creatives insufficient for consistent effectiveness, underlining the importance of personalization. In practice, most personalized video advertising systems follow a retrieval-based paradigm, selecting the optimal one from a small set of professionally pre-produced creatives for each user. Such static and finite inventories limit both the granularity and the timeliness of personalization, and prevent the creatives from being continuously refined based on online user feedback. Recent advances in generative AI make it possible to move beyond retrieval toward optimizing video creatives in a continuous space at serving time. In this light, we propose NextAds, a generation-based paradigm for next-generation personalized video advertising. We articulate its key principles and conceptualize an agentic workflow for closed-loop creative optimization, spanning planning, generation, verification, and reflection. We further explore how personalized creatives can evolve across impressions through feedback-driven refinement and progressive storytelling. To further ground this vision, we formalize two representative tasks and conduct exploratory experiments on corresponding lightweight benchmarks, revealing both the promise and limitations of existing GenAI. Moreover, we discuss potential research opportunities and the broader implications of increasingly personalized and native advertising, highlighting issues that warrant attention from the community and aiming to provide insights for future research and responsible deployment. Our code, benchmarks, and demonstrations are available at https://anonymous.4open.science/w/NextAdsProjectPage/.