Position: Recommender Systems Should Move Beyond\\Platform-Centric Ranking toward Personal Agent-Mediated Recommendation

作者：H-J Yuan, Peng He, Dan Zhang, J.F. Liang, J ZHU · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.21627117 · 研究领域：Recommender Systems and Techniques、Explainable Artificial Intelligence (XAI)、Ethics and Social Impacts of AI

Recommender systems are usually framed as ranking systems: platforms observe users, construct candidate sets, and select items on their behalf. This framing hides a deeper allocation of control, in which platforms also determine candidate access, evidence boundaries, explanations, and the path from user need to recommended output. We argue that the next bottleneck in recommendation is not only preference modeling, but control over evidence acquisition and disclosure. We argue for \textbf{Personal Agent-Mediated Recommendation} (PAMR), a paradigm in which a user-facing personal agent represents the user in discovering, filtering, aggregating, and governing recommendation evidence across distributed sources. The central shift is not simply from one ranking model to another, but from platform-side item ranking to user-side evidence mediation. As a position paper, we define PAMR as a new recommendation paradigm, establish its boundary criteria, identify its core mediation decisions, and propose a mediation-centered evaluation framework. A proof-of-concept study on hard Yelp restaurant recommendation tasks further shows that, under a shared LLM ranker, source selection and controlled disclosure provide the strongest observed utility-traceability-exposure-cost operating point.