# NextAds: Towards Next-generation Personalized Video Advertising

arXiv: https://arxiv.org/abs/2603.02137 · HTML: https://mlsanity.com/paper/2603.02137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation](https://mlsanity.com/paper/2412.00596/) (2024)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [A Content-Driven Micro-Video Recommendation Dataset at Scale](https://mlsanity.com/paper/2309.15379/) (2023)
- [Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://mlsanity.com/paper/2503.02450/) (2025)
- [SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation](https://mlsanity.com/paper/2504.12722/) (2025)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement](https://mlsanity.com/paper/2008.07467/) (2020)
- [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024)
- [A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model](https://mlsanity.com/paper/2401.10934/) (2024)
- [Auto-bidding and Auctions in Online Advertising: A Survey](https://mlsanity.com/paper/2408.07685/) (2024)
- [Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism](https://mlsanity.com/paper/2402.10548/) (2024)
- [Automated Creative Optimization for E-Commerce Advertising](https://mlsanity.com/paper/2103.00436/) (2021)
- [CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessment](https://mlsanity.com/paper/2407.21553/) (2024)
- [DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition](https://mlsanity.com/paper/2504.17349/) (2025)
- [VC-LLM: Automated Advertisement Video Creation from Raw Footage using Multi-modal LLMs](https://mlsanity.com/paper/2504.05673/) (2025)

## Abstract (arXiv)

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.