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Founder's Pitch
"Enhance app store relevance with LLM-generated textual judgments for improved search ranking."
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1/4 signals
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Series A Potential
3/4 signals
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Why It Matters
This research enables more accurate app ranking in digital marketplaces by employing LLM-generated labels, which improves both text and user-behavior relevance without requiring large quantities of human-generated data.
Product Angle
Develop an API that offers LLM-generated relevance labels for digital content platforms, allowing easy integration to enhance the ranking capabilities of search engines.
Disruption
It replaces the traditional human-dependent relevance labeling process with an automated, scalable solution that reduces operational costs and enhances performance metrics.
Product Opportunity
The digital marketplace industry is vast and continuously growing, with companies willing to pay for solutions that enhance user engagement and satisfaction. This approach provides a cost-effective means to achieve higher conversion rates by improving search relevance.
Use Case Idea
Commercialize this as a B2B service for digital marketplaces to enhance their search rankings using LLM-generated relevance labels, thus increasing conversions and user satisfaction.
Science
The paper describes a method to overcome the scarcity of textual relevance labels in app store rankings by using fine-tuned large language models (LLMs) to generate these labels at scale. This provides additional training data for the ranker, which improves both its textual and behavioral relevance through a multi-objective optimization framework.
Method & Eval
The method uses a fine-tuned 3 billion parameter LLM to generate textual relevance labels. These labels are integrated into a machine learning ranker which is then validated through both offline (NDCG metrics) and online (A/B tests), showing improved conversion rates.
Caveats
The effectiveness relies heavily on the quality of the fine-tuning process and the initial human judgments used. Also, only proven in the context of the App Store; generalization to other contexts is pending.
Author Intelligence
Evangelia Christakopoulou
LEAD Apple
echristakopoulou@apple.com
Vivekkumar Patel
Apple
vpatel22@apple.com
Hemanth Velaga
Apple
h_velaga@apple.com
Sandip Gaikwad
Apple
sandip_gaikwad@apple.com