Position Auctions in AI-Generated Content

TL;DR

This paper extends position auctions to AI-generated content, incorporating context-aware sponsored creatives and modeling complex user interactions to optimize welfare and revenue.

Contribution

It formalizes a new auction model for AI content with context-sensitive placements and develops mechanisms for different user behavior models.

Findings

Efficient optimal mechanisms for the MNL model.

Approximate solutions for the cascade model.

Formal mathematical framework for context-aware position auctions.

Abstract

We consider an extension to the classic position auctions in which sponsored creatives can be added within AI generated content rather than shown in predefined slots. New challenges arise from the natural requirement that sponsored creatives should smoothly fit into the context. With the help of advanced LLM technologies, it becomes viable to accurately estimate the benefits of adding each individual sponsored creatives into each potential positions within the AI generated content by properly taking the context into account. Therefore, we assume one click-through rate estimation for each position-creative pair, rather than one uniform estimation for each sponsored creative across all positions in classic settings. As a result, the underlying optimization becomes a general matching problem, thus the substitution effects should be treated more carefully compared to standard position auction settings, where the slots are independent with each other. In this work, we formalize a concrete mathematical model of the extended position auction problem and study the welfare-maximization and revenue-maximization mechanism design problem. Formally, we consider two different user behavior models and solve the mechanism design problems therein respectively. For the Multinomial Logit (MNL) model, which is order-insensitive, we can efficiently implement the optimal mechanisms. For the cascade model, which is order-sensitive, we provide approximately optimal solutions.