@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 ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs
%A Kim, Kyungho
%A Choi, Yeonje
%A Hwang, Gyurim
%A Chung, Sejin
%A Lee, Hongseok
%A Song, Myeong Ho
%A Kim, Yeongho
%A Kim, Sunwoo
%A Lee, Jongha
%A Kim, Juyeon
%A Shin, Kijung
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F kim-etal-2026-pre
%X 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.
%U https://aclanthology.org/2026.acl-industry.28/
%P 420-435
Markdown (Informal)
[Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs](https://aclanthology.org/2026.acl-industry.28/) (Kim et al., ACL 2026)
ACL
- Kyungho Kim, Yeonje Choi, Gyurim Hwang, Sejin Chung, Hongseok Lee, Myeong Ho Song, Yeongho Kim, Sunwoo Kim, Jongha Lee, Juyeon Kim, and Kijung Shin. 2026. Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 420–435, San Diego, California, USA. Association for Computational Linguistics.