Sponsored Questions and How to Auction Them
Kshipra Bhawalkar, Alexandros Psomas, Di Wang
TL;DR
The paper tackles how to allocate sponsored prompts (questions) in interactive platforms where user intent is ambiguous. It compares an end-to-end DSIC VCG mechanism that jointly optimizes the sponsored question and the downstream ad auction with a modular two-stage design that decouples the question auction from the ad auction, analyzing incentive properties and efficiency. The key finding is that the end-to-end mechanism is truthful and welfare-maximizing, whereas the modular approach can incur unbounded inefficiency (Price of Anarchy), with this inefficiency persisting even under alternative first-price or all-pay variants. These results highlight the value of integrated design for sponsored suggestions in conversational AI contexts and motivate future work on revenue-focused objectives and more complex, multi-round interaction settings.
Abstract
Online platforms connect users with relevant products and services using ads. A key challenge is that a user's search query often leaves their true intent ambiguous. Typically, platforms passively predict relevance based on available signals and in some cases offer query refinements. The shift from traditional search to conversational AI provides a new approach. When a user's query is ambiguous, a Large Language Model (LLM) can proactively offer several clarifying follow-up prompts. In this paper we consider the following: what if some of these follow-up prompts can be ``sponsored,'' i.e., selected for their advertising potential. How should these ``suggestion slots'' be allocated? And, how does this new mechanism interact with the traditional ad auction that might follow? This paper introduces a formal model for designing and analyzing these interactive platforms. We use this model to investigate a critical engineering choice: whether it is better to build an end-to-end pipeline that jointly optimizes the user interaction and the final ad auction, or to decouple them into separate mechanisms for the suggestion slots and another for the subsequent ad slot. We show that the VCG mechanism can be adopted to jointly optimize the sponsored suggestion and the ads that follow; while this mechanism is more complex, it achieves outcomes that are efficient and truthful. On the other hand, we prove that the simple-to-implement modular approach suffers from strategic inefficiency: its Price of Anarchy is unbounded.