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Talent Scout
H
Haibo Jin
University of Illinois at Urbana-Champaign
R
Ruoxi Chen
Starc Institute
P
Peiyan Zhang
Hong Kong University of Science and Technology
Y
Yifeng Luo
University of Illinois at Urbana-Champaign
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References
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Founder's Pitch
"Optimize product visibility by controlling LLM-based search output rankings using CORE."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research addresses the challenge of biased visibility for products in LLM-based search engines, which can disadvantage small businesses by limiting their exposure relative to larger competitors.
Product Angle
Develop an API or integration service for e-commerce platforms that allows product sellers to optimize how their products are ranked in LLM search results.
Disruption
This method could replace traditional SEO techniques by enabling more direct control over product ranking in search results, potentially shifting paradigms for e-commerce visibility strategies.
Product Opportunity
The burgeoning e-commerce platform market is highly competitive, and sellers continuously seek new SEO or ranking advantages, offering a substantial market for tools that can boost their visibility in generative search engines.
Use Case Idea
A platform for e-commerce sellers to enhance the visibility of their products in LLM-driven search engines like those used on Amazon.
Science
The paper introduces CORE, a method for controlling output rankings in LLM-based searches by optimizing content returned from search engines. It employs an optimization algorithm that uses various content strategies (string, reasoning, review) to adjust how items appear in LLM-generated lists.
Method & Eval
The evaluation used a benchmark (ProductBench), measuring the CORE method's performance on four LLMs showing significantly improved ranking control with high success rates across various product categories.
Caveats
The method relies heavily on black-box assumptions about LLM behavior, which may limit its effectiveness across different models or future modifications to LLM architectures.
Author Intelligence
Haibo Jin
University of Illinois at Urbana-Champaign
Ruoxi Chen
Starc Institute
Peiyan Zhang
Hong Kong University of Science and Technology
Yifeng Luo
University of Illinois at Urbana-Champaign
Huimin Zeng
University of Illinois at Urbana-Champaign
Man Luo
Intel Labs
Haohan Wang
University of Illinois at Urbana-Champaign
haohanw@illinois.edu