Meta Proposes PAMO to Optimize Cross-App Recommendations via Personal Agents
Meta introduces PAMO, a training method enabling personal AI agents to enhance app recommendations using cross-platform user history. By masking external data to assess dependency and rewarding only accuracy-improving overrides, the approach outperforms platform baselines and standard RL in hit rates.
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Rohan Paul (@rohanpaul_ai) · 2026-10-31 16
New Meta paper shows that a personal AI agent can improve an app's recommendations with your history from other apps, if it learns when to defer.
Each app ranks items using only what you did there, plus patterns from other users. A personal agent sees activity across apps, but it can also break a good ranking by dropping the right item.
They built PAMO, a training method that hides the cross-app history to see which of the agent's answers depend on it. Answers that lean on that history get more credit, but only if ranking accuracy does not drop.
On real Steam users with Nintendo and Xbox history, a 4B PAMO agent reached 47.9% top-10 hit rate, versus 40.8% for the platform. It also beat standard RL training on all 5 test sets.
Personal agents that edit other apps' results should defer by default and override only when the user's history gives specific evidence.
– arxiv. org/abs/2610.07588
Title: "Personal-Agent Mediated Recommendation with Cross-Platform User History"
