AI

Personal-Agent Mediated Recommendation with Cross-Platform User History

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 03:18:24 UTC.

Briefings

A new research framework called Personal-Agent Mediated Recommendation (PAMR) formalizes a paradigm where a personal LLM agent mediates between platform rankings and user history. The study introduces MediateRec, a benchmark testing this mediation, and Personal Attribution Mediation Optimization (PAMO), an algorithm that balances rescuing platform-recommended items against harmful overrides from proprietary LLMs. Experiments show PAMO improves outcomes across seen and unseen platforms while achieving a better rescue-harm balance compared to outcome-only methods.

Key points

• The study introduces MediateRec, a benchmark that includes scalable proxy environments and a real cross-platform test under a controlled platform-agent information boundary.
• PAMO counterfactually masks user history to estimate personal mediation support and reallocates rank-aware advantage mass under a platform-relative value floor.

Context

Recommendation systems are shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent acts on the user's behalf across services. This research formalizes the emerging paradigm of Personal-Agent Mediated Recommendation.

Limitations

The study relies on theoretical proofs and controlled experiments; the real cross-platform test is limited by the controlled platform-agent information boundary, and the benchmark includes proxy environments that may not fully reflect real-world conditions.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 24/10/2026, 23:31:53 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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