{
  "id": 14161,
  "url": "https://arxiv.org/abs/2607.25253v1",
  "title": "The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape",
  "summary": "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 creat",
  "authors": "Deyao Hong, Kehan Zheng, Qian Li, Jun Zhang, Jie Jiang, Hongning Wang",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T03:54:38.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14161",
  "original_url": "https://arxiv.org/abs/2607.25253v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}