Meta 论文提出 PAMO：个人智能体用跨平台历史改进应用推荐
Meta 把推荐系统和个人智能体结合了：4B 小模型用你的 Steam、Nintendo、Xbox 历史把命中率从 40.8% 提到 47.9%，论文还讲了什么时候该出手覆盖原推荐。
Meta 发表论文提出 PAMO 训练方法，让个人智能体基于用户在其他应用的历史来改进当前应用的推荐排名。该方法通过隐藏跨应用历史来判断智能体答案是否真正依赖这些信息，只在排名准确率不下降时给予奖励。在真实 Steam 用户数据上加入 Nintendo 和 Xbox 历史，4B 参数的 PAMO 智能体达到 47.9% 的 top-10 命中率，平台原始推荐为 40.8%，并在全部 5 个测试集上超过标准 RL 训练。结论是智能体应默认遵从应用原有排名，仅在用户历史提供明确证据时才覆盖。
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"
