One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation

One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/one-size-many-fits-aligning-diverse-group-wise-click-preferences-in-large-scale-advertising-image-generation

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Proof freshness: stale
Proof status: partial
Display score: 9/10
Last proof check: 2026-03-17
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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Canonical ID one-size-many-fits-aligning-diverse-group-wise-click-preferences-in-large-scale-advertising-image-generation | Route /signal-canvas/one-size-many-fits-aligning-diverse-group-wise-click-preferences-in-large-scale-advertising-image-generation

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation

PDF: https://arxiv.org/pdf/2602.02033v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-17T19:46:04.153Z

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Subject: One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
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.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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.
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Implication not extracted yet.
Verificationpartial
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Evidencepartial
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.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
The framework's performance is dependent on the quality and relevance of the original GAIP dataset.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics
Implicationpartial
Directly stated in the abstract with clear description.
Verificationpartial
partial
Evidencepartial
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
Implicationpartial
Directly stated in abstract and supported by analysis mentioning significant improvement in group-wise CTR.
Verificationpartial
partial
Evidencepartial
the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users
Implicationpartial
Explicitly stated in abstract with specific numbers.
Verificationpartial
partial
Evidencepartial
Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings
Implicationpartial
Directly stated in abstract and supported by analysis mentioning extensive experiments.
Verificationpartial
partial
Evidencepartial
The framework's performance is dependent on the quality and relevance of the original GAIP dataset.
Implicationpartial
Explicitly stated in the analysis caveats section.
Verificationpartial
partial

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One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation

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