NextAds: Towards Next-generation Personalized Video Advertising
Yiyan Xu, Ruoxuan Xia, Wuqiang Zheng, Fengbin Zhu, Wenjie Wang, Fuli Feng

TL;DR
NextAds introduces a generative AI-based approach for personalized video advertising, moving beyond static retrieval methods to enable dynamic, continuous creative generation and integration tailored to individual users.
Contribution
The paper proposes a novel generation-based paradigm for personalized video ads, formulates two benchmark tasks, and demonstrates initial feasibility with prototype pipelines.
Findings
Generative AI can produce personalized video creatives effectively.
Prototype pipelines show promising initial results.
Highlights key challenges and future opportunities.
Abstract
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 makes 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 limits both the granularity and the timeliness of personalization, and prevents 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…
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Taxonomy
TopicsRecommender Systems and Techniques · Artificial Intelligence in Games · Innovative Human-Technology Interaction
