From conversations to mechanisms: aligning advertiser Incentives in ai-powered product recommendations

This research paper explores the development of efficient recommendation systems, such as AI shopping assistants, that manage multi-round interactions between a platform, advertisers, and users. The authors address a fundamental challenge: advertisers possess private, multi-dimensional information about both their own profit values and the user's preferences, creating incentives to manipulate recommendations. To solve this, the study introduces data-driven dynamic team mechanisms that align these conflicting incentives by conditioning advertiser payments on real-time user feedback. By utilizing behavioral signals like purchases and follow-up queries, the platform can create unbiased estimators of user tastes to ensure the most socially beneficial products are suggested. The proposed framework guarantees that advertisers act truthfully while maintaining individual participation and budget surplus for the platform. Ultimately, the paper demonstrates how the conversational nature of generative AI provides a unique stream of data that overcomes traditional economic barriers to efficiency in digital marketplaces.