NextAds: Towards Next-generation Personalized Video Advertising

• URL: http://arxiv.org/abs/2603.02137v1
• Date: Mon, 02 Mar 2026 17:58:07 GMT
• Title: NextAds: Towards Next-generation Personalized Video Advertising
• Authors: Yiyan Xu, Ruoxuan Xia, Wuqiang Zheng, Fengbin Zhu, Wenjie Wang, Fuli Feng,
• Abstract summary: NextAds is a generation-based paradigm for next-generation personalized video advertising.<n>We formulate two representative tasks: personalized creative generation and personalized creative integration.<n>We conduct initial exploratory experiments, demonstrating that GenAI can generate and integrate personalized creatives with encouraging performance.
• Score: 46.666292153282875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization [15.51942931334223]
  In e-commerce advertising, selecting the most compelling combination of creative elements is critical for capturing user attention and driving conversions.<n>We propose a novel framework named GenCO that integrates generative modeling with multi-instance reward learning.<n>Our approach has significantly increased advertising revenue, demonstrating its practical value.
  arXiv  Detail & Related papers  (2025-08-13T11:53:41Z)
• AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation [50.63646953706144]
  We introduce AniMaker, a framework enabling efficient multi-candidate clip generation and storytelling-aware clip selection.<n>AniMaker achieves superior quality as measured by popular metrics including VBench and our proposed AniEval framework.
  arXiv  Detail & Related papers  (2025-06-12T10:06:21Z)
• POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation [33.03808810462489]
  State-of-the-art visual generative AI tools hold immense potential to assist users in the early ideation stages of creative tasks.<n>Many large-scale text-to-image systems are designed for broad applicability, yielding conventional output that may limit creative exploration.<n>We introduce POET, a real-time interactive tool that automatically discovers dimensions of homogeneity in text-to-image generative models.
  arXiv  Detail & Related papers  (2025-04-18T00:54:36Z)
• CTR-Driven Advertising Image Generation with Multimodal Large Language Models [53.40005544344148]
  We explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective.<n>To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL)<n>Our method achieves state-of-the-art performance in both online and offline metrics.
  arXiv  Detail & Related papers  (2025-02-05T09:06:02Z)
• Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
  Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP) We develop a generic and personalization generative framework, that can handle a wide range of personalized needs. Our methodology enhances the capabilities of foundational language models for personalized tasks.
  arXiv  Detail & Related papers  (2024-03-15T20:21:31Z)
• A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model [8.945197427679924]
  Traditional AI-based approaches face the same problem of not considering user information while having limited aesthetic knowledge from designers. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. This paper proposes a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage.
  arXiv  Detail & Related papers  (2024-01-17T03:27:39Z)
• Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems [20.78133992969317]
  We propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking. The online architecture enables sophisticated personalized creative modeling while reducing overall latency. The offline joint model for CTR estimation allows mutual awareness and collaborative optimization between ads and creatives.
  arXiv  Detail & Related papers  (2023-12-20T04:05:21Z)
• AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting [7.515971669919419]
  In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. We introduce the task of generative models for creative generation that incorporate user interests, and itshape AdBooster, a model for personalized ad creatives.
  arXiv  Detail & Related papers  (2023-09-08T12:57:05Z)
• Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts [116.05656635044357]
  We propose a generic video editing framework called Make-A-Protagonist. Specifically, we leverage multiple experts to parse source video, target visual and textual clues, and propose a visual-textual-based video generation model. Results demonstrate the versatile and remarkable editing capabilities of Make-A-Protagonist.
  arXiv  Detail & Related papers  (2023-05-15T17:59:03Z)
• Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
  We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
  arXiv  Detail & Related papers  (2022-07-25T14:37:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.