@inproceedings{kim-etal-2026-pre,
    title = "Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with {VLM}s",
    author = "Kim, Kyungho  and
      Choi, Yeonje  and
      Hwang, Gyurim  and
      Chung, Sejin  and
      Lee, Hongseok  and
      Song, Myeong Ho  and
      Kim, Yeongho  and
      Kim, Sunwoo  and
      Lee, Jongha  and
      Kim, Juyeon  and
      Shin, Kijung",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.acl-industry.28/",
    pages = "420--435",
    ISBN = "979-8-89176-394-4",
    abstract = "Vision-Language Models (VLMs) perform well on general multimodal tasks, yet applying them to real-world advertisement (ad) evaluation is challenging due to strong brand specificity and limited labeled data. We introduce a new practical task, brand-specific ad ranking, which aims to rank ads for a target brand prior to deployment by modeling brand-specific effectiveness. To this end, we propose ADvisor, which derives explicit brand-aware decision criteria using VLMs, augments limited brand context with ads from similar brands, and applies reflection-based scoring for ranking. Experiments on real-world advertising data from 10 brands, collected through actual ad campaigns, show that ADvisor outperforms strong baselines by up to 7.2{\%}. Further analyses show the generated criteria capture meaningful brand specificity, and ADvisor also performs strongly in online A/B testing. Our code is available at https://github.com/K-Kyungho/ADvisor."
}