The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape

2026-07-27

AI Summary

This study introduces the concept of an “agent-mediated recommendation market,” wherein large language model (LLM)-powered user agents enable users to proactively articulate preferences, prompting competing platforms to provide personalized recommendations and justifications. The authors propose the first experimental framework based on LLM-driven user agents, simulating platform competition across three product domains. Integrating controlled experiments, strategic explanation generation, and a feedback loop, they jointly examine the design of access, attention allocation, and accountability mechanisms. Findings reveal that, in competitive settings, platforms rely on selectively positive explanations for 73–78% of top-ranked recommendations; however, incorporating user feedback reduces this reliance to 36–41% and significantly increases the likelihood of users purchasing relevant items, highlighting a tension between broadening access and achieving effective exposure.

Abstract

Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents enable a different recommendation process: a user specifies a need before choosing a platform, leaving platforms to compete for the user's attention, which we refer to as an agentic recommendation market. In our controlled LLM-based experiments across three product domains, we find this new setting of recommendation creates a tension between access and attention. Compared with traditional platform-centric recommendation, user-centric recommendation greatly expands the opportunity for relevant items to enter comparison; yet broader participation does not translate directly into effective exposure. Competition directly triggers platforms' strategic play: selectively positive explanations occupy 73--78% of first-ranked positions. When the user agent relates platforms' actions to subsequent user feedback, this share falls to 36--41%, while the chance of a user purchasing the relevant item increases. A user agent is therefore more than a ranker over a larger pool of candidates: its querying, ranking, and feedback mechanism governing who can compete, how scarce attention is allocated, and how earlier outcomes shape the evaluation of platforms directly affect user utility. Designing agentic recommendation therefore requires treating access, attention, and accountability as a joint mechanism design problem.

Problem

• agentic recommendation
• attention competition
• user-centric recommendation
• platform strategy
• mechanism design

Innovation

• agentic recommendation
• LLM-based user agents
• platform competition
• mechanism design
• attention allocation