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ARXIV:2602.02033 · AI IN ADVERTISING · SUBMITTED 17 MAR · 19:46 UTC · FRESHNESS STALE
ARXIV:2602.02033AI IN ADVERTISINGSUBMITTED 17 MAR · 19:46 UTCFRESHNESS STALEarXiv
A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness.
Opportunity summary
Pain A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence partial
A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness.
Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings.
AI in Advertising moved forward this cycle; last verified April 2026. Public score 9.0/10.
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A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness.
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10.48550/arXiv.2602.02033A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness.
Abstract
Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness. To bridge this gap, we present \textit{One Size, Many Fits} (OSMF), a unified framework that aligns diverse group-wise click preferences in large-scale advertising image generation. OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics, representing each group with rich collective preference features. Building on these groups, preference-conditioned image generation employs a Group-aware Multimodal Large Language Model (G-MLLM) to generate tailored images for each group. The G-MLLM is pre-trained to simultaneously comprehend group features and generate advertising images. Subsequently, we fine-tune the G-MLLM using our proposed Group-DPO for group-wise preference alignment, which effectively enhances each group's CTR on the generated images. To further advance this field, we introduce the Grouped Advertising Image Preference Dataset (GAIP), the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings. Our code and datasets will be released at https://github.com/JD-GenX/OSMF.
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Proof status
partial0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Dimensions overall score 9.0
PROBLEM
A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness.
METHOD
Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads to suboptimal p...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings.
WHY NOW
AI in Advertising moved forward this cycle; last verified April 2026. Public score 9.0/10.
To bridge this gap, we present \textit{One Size, Many Fits} (OSMF), a unified framework that aligns diverse group-wise click preferences in large-scale advertising image generation.
Implication not extracted yet.
partial
OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics, representing each group with rich collective preference features.
Implication not extracted yet.
partial
Building on these groups, preference-conditioned image generation employs a Group-aware Multimodal Large Language Model (G-MLLM) to generate tailored images for each group.
Implication not extracted yet.
partial
Subsequently, we fine-tune the G-MLLM using our proposed Group-DPO for group-wise preference alignment, which effectively enhances each group's CTR on the generated images.
Implication not extracted yet.
partial
To further advance this field, we introduce the Grouped Advertising Image Preference Dataset (GAIP), the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users.
Implication not extracted yet.
partial
Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings.
Implication not extracted yet.
partial
The framework's performance is dependent on the quality and relevance of the original GAIP dataset.
Implication not extracted yet.
partial
OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics
Directly stated in the abstract with clear description.
partial
fine-tune the G-MLLM using our proposed Group-DPO for group-wise preference alignment, which effectively enhances each group's CTR on the generated images
Directly stated in abstract and supported by analysis mentioning significant improvement in group-wise CTR.
partial
the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users
Explicitly stated in abstract with specific numbers.
partial
Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings
Directly stated in abstract and supported by analysis mentioning extensive experiments.
partial
The framework's performance is dependent on the quality and relevance of the original GAIP dataset.
Explicitly stated in the analysis caveats section.
partial
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A framework to tailor advertising images for diverse user groups, boosting CTR and ad effectiveness.
Segment
AI in Advertising
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Commercial read
9.0/10 public viability
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stale
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stale
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Technical feasibility
partial
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Gaps
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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