Utility-Aware AI Framework Optimizes Product Images for Sales
A study published on arXiv introduces a multimodal contrastive learning framework that takes consumer demand into account for image generation. While current generative AI models connect images to text prompts, they fail to enhance marketplace outcomes. This new framework employs a Utility-Aware InfoNCE loss to steer the generation process toward images that are not only semantically relevant but also boost demand. The effectiveness of this method is achieved by adjusting the learned image-text representation space to focus on demand-oriented visual signals, supported by theoretical limits. Practical applications on platforms like Amazon and Airbnb showcase the framework's capabilities.
Key facts
- arXiv paper 2605.28733 proposes utility-aware multimodal contrastive learning
- Existing generative AI models do not directly optimize marketplace performance
- Utility-Aware InfoNCE loss incorporates consumer demand into image generation
- Framework guides generation toward semantically coherent and demand-enhancing images
- Shift in representation space toward demand-driven visual cues validated theoretically
- Downstream applications tested on Amazon and Airbnb
Entities
Institutions
- arXiv
- Amazon
- Airbnb