NextAds: Towards Next-generation Personalized Video Advertising

2026-03-02

AI Summary

This work proposes NextAds, the first generative framework for personalized video advertising, addressing the limitations of traditional approaches that rely on static asset libraries and struggle to achieve fine-grained, real-time personalization and online optimization. By dynamically generating and integrating personalized creative content at serving time, NextAds overcomes the constraints of conventional retrieval-based methods. The paper formalizes two core tasks—personalized creative generation and integration—and introduces a lightweight, end-to-end pipeline as a reproducible benchmark. Preliminary experiments demonstrate that generative AI–based approaches can efficiently produce high-quality personalized advertisements, exhibiting significant advantages in both performance and practical applicability.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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 paradigm for next-generation personalized video advertising, and conceptualize NextAds with four core components. To enable comparable research progress, we formulate two representative tasks: personalized creative generation and personalized creative integration, and introduce corresponding lightweight benchmarks. To assess feasibility, we instantiate end-to-end pipelines for both tasks and conduct initial exploratory experiments, demonstrating that GenAI can generate and integrate personalized creatives with encouraging performance. Moreover, we discuss the key challenges and opportunities under this paradigm, aiming to provide actionable insights for both researchers and practitioners and to catalyze progress in personalized video advertising.

Problem

• personalized video advertising
• generative AI
• ad creative generation
• retrieval-based paradigm
• online user feedback

Innovation

• generative AI
• personalized video advertising
• creative generation
• continuous optimization
• ad integration