[
    {
        "id": "osp-15697",
        "type": "article-journal",
        "title": "Personal-Agent Mediated Recommendation with Cross-Platform User History",
        "author": [
            {
                "family": "Xia",
                "given": "Yu"
            },
            {
                "family": "Zhang",
                "given": "Jiangfan"
            },
            {
                "family": "Xiao",
                "given": "Jun"
            },
            {
                "family": "McAuley",
                "given": "Julian"
            },
            {
                "family": "Fan",
                "given": "Xiangjun"
            }
        ],
        "URL": "https://omanscience.com/en/articles/personal-agent-mediated-recommendation-with-cross-platform-user-history",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the user's behalf across services. We formalize this emerging paradigm as Personal-Agent Mediated Recommendation: a platform recommender ranks a candidate set using platform-local information, and a personal agent uses user-authorized cross-platform history to mediate the resulting ranking and produce the final top-K slate. Such mediation is nontrivial: the platform ranking can encode strong population evidence that the personal agent cannot observe, so effective mediation must therefore balance beneficial rescues against harmful overrides. To study this trade-off, we introduce MediateRec, a benchmark that includes scalable proxy cross-platform environments and a real cross-platform test under a controlled platform-agent information boundary. To train the agent to use cross-platform history effectively, we further propose Personal Attribution Mediation Optimization (PAMO), which counterfactually masks that history to estimate personal mediation support and reallocates rank-aware advantage mass under a platform-relative value floor. We theoretically prove that PAMO preserves cutoff-level advantage mass and is locally optimal among first-order reallocations that preserve this mass without lowering average platform-relative value. Experiments on MediateRec show that personal-agent mediation enables meaningful platform corrections, yet even strong proprietary LLMs introduce non-negligible harmful overrides. PAMO consistently improves over matched outcome-only RL across seen and unseen target platforms and on the real cross-platform test, while achieving a better rescue-harm balance."
    }
]