Toward User-centered Agentic Recommendation: When Does Platform Competition Benefit Users?

Conventional recommender systems operate within platform-specific catalogs, limiting users' access to potentially valuable resources across platforms. We propose user-centered agentic recommendation, a paradigm in which personal user agents compare recommendations from competing platforms, and formalize their interaction as a repeated game. Platforms can strategically modify item descriptions in order to increase their perceived competitiveness, while user agents maintain each platform's credibility assessment using feedback from consumed items only. Our analysis shows that expanded resource access need not improve user welfare: strategic misrepresentation and limited verification can offset the benefits of platform competition. We quantify when platform credibility assessment allows users to benefit from broader access despite platform's strategic exaggeration. Our analysis also identifies conditions that guarantee higher expected welfare than the original platform-centered recommendation setup, with an interaction budget linear to the number of platforms. Experiments on synthetic and Amazon-grounded markets show that reputation can improve finite-horizon utility, while its benefits depend on market size and mechanism parameters.