User-centered agentic recommendation: when platform competition fails to benefit users
_reachsumit · x · 2026-11-05
- An arXiv paper proposes user-centered agentic recommendation: user agents compare recommendations from competing platform agents, modeled as a repeated game.
- Platforms can strategically exaggerate descriptions without changing intrinsic item value, while user agents assess credibility only from consumed-item feedback.
- Key finding: broader resource access need not improve user welfare — strategic misrepresentation and limited verification can offset competition's benefits.
- The paper quantifies conditions where credibility assessment yields higher average welfare with an interaction budget linear in platform count; experiments on synthetic and Amazon-grounded markets confirm finite-horizon utility gains depend on market size and mechanism parameters.
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