Position Auctions in AI-Generated Content

摘要

We consider an extension to classic position auctions in which sponsored creatives are embedded within AI-generated content rather than shown in predefined slots. Leveraging advanced LLM technologies, it becomes viable to seamlessly integrate sponsored creatives with AI content and accurately estimate the context-aware benefits of differing insertion positions. However, this approach introduces novel challenges; substitution effects require rigorous treatment compared to standard position auction settings, where slots are independent of each other. In this work, we formalize a mathematical model of the extended position auction problem and study the welfare- and revenue-maximization mechanism design problems. We assume a specific click-through rate estimation for each position-creative pair and consider two user behavior models: a multinomial logit (MNL) and a cascade model. For the MNL model, which is order-insensitive, we efficiently implement optimal mechanisms. For the cascade model, which is order-sensitive, we provide approximately optimal mechanisms.