Abstract
A system and method for synthesizing objective evaluations for personalized sponsored content in generative search environments is described. The technology utilizes a multi-voice generation pipeline to mediate between structured advertiser payloads and dynamic organic responses. An explainer model dynamically generates a neutral, objective summary of an advertisement that matches the style of an organic conversational flow. The system may classify advertiser data into factual, descriptive, and subjective claims, applying different grounding and attribution techniques to each category. Furthermore, a post-auction quality gate may dynamically suppress advertisements if the generated objective explanation falls below a helpfulness threshold. Keywords: generative artificial intelligence, sponsored content, objective synthesis, dynamic evaluation, conversational interface, personalized search.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Whalin, Tim; Noskey, Grant; Goel, Gagan; Muralidharan, Omkar; Gupta, Shrey; Oh, Grace; Monkman, Chris; Rostoum, Noor; Kamal, Hanny; and Liu, Chen, "Objective Evaluation Synthesis for Personalized Sponsored Content in Generative Search", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10170