Context-Aware Criteria Generation with VLMs for Advertisement Ranking under Data Scarcity

Kyungho Kim, Yeonje Choi, Gyurim Hwang, Sejin Chung, Hongseok Lee, Myeong Ho Song, Yeongho Kim, Sunwoo Kim, Jongha Lee, Juyeon Kim, Kijung Shin

Keywords: Vision Language Models, Advertisement Ranking, Data Scarcity, Reflection

Abstract: Vision-Language Models (VLMs) perform strongly on generic multimodal reasoning tasks, yet real-world business decisions require reasoning that depends on the specific business objective under very limited data and labels. We introduce a new task, brand-specific advertisement ranking, which aims to rank ads for a target brand under these constraints. This task requires capturing what makes an ad effective for that particular brand, rather than relying on generic visual-textual cues or large-scale supervision. To tackle this task, we propose ADvisor, a novel approach that derives explicit brand-aware decision criteria with careful use of VLMs. ADvisor also augments the limited brand-specific context with ads from similar brands, and uses the generated criteria for reflection-driven scoring to rank ads. Experiments on real-world advertising data from 10 brands, which include performance labels for evaluation, collected from actual ad campaigns, show that ADvisor outperforms the strongest baseline by up to 7.2% in ranking performance. ADvisor also performs strongly in online A/B testing, even when compared with human experts. In addition, both qualitative and quantitative analyses confirm that the generated criteria capture effective brand-specific evaluation standards rather than generic ones. Our code is available at https://github.com/K-Kyungho/ADvisor.

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Submission Number: 48