Abstract
Generative artificial intelligence (AI) systems frequently produce structured lists by synthesizing web data. Inserting ads into such lists can lead to jarring disruptions or redundant entries when integrated into these dynamic outputs. This creates a challenge in incorporating sponsored content without compromising user experience or trust, particularly when a relevant advertisement duplicates an organically generated item. The technology introduces a system that dynamically integrates sponsored elements into AI-generated lists by first employing a large language model to reverse-engineer the list’s context and derive one or more granular synthetic queries. This allows for the selection of sponsored candidates based on an equivalence score derived from commerciality, local and global context checks, and semantic similarity to organic items. To address instances of duplication, an AI-driven engine actively mutates the sponsored element’s presentation, adding distinct, differentiating information such as real-time inventory data or social proof, rather than simply omitting or duplicating the item. The system also ensures visual and cognitive integration by dynamically aligning the sponsored content’s structure and voice with the organic list’s formatting, utilizing an “Objective Explainer” for neutral commentary alongside a distinct “Advertiser Voice” for promotional messaging. Keywords: LLM-generated lists, dynamic ad insertion, context-aware advertising, personalized sponsored elements, duplication resolution, structural alignment, voice alignment
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Whalin, Tim; Goel, Gagan; Monkman, Chris; Gupta, Shrey; Kamal, Hanny; Galep, Jason; and Kothaneth, Shreya, "Dynamic Context-Aware and Personalized Insertion of Sponsored Elements into LLM-Generated Lists", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10172