研究人员开发了一种新算法 MO-PQUCB，旨在改进多目标老虎机问题中的个性化决策。该算法独特地利用用户的主动对话式查询，例如“便宜又干净”选项的请求，以更好地理解他们的偏好。通过将这些结构化的偏好信号与传统的老虎机反馈相结合，MO-PQUCB 旨在加速偏好估计并减少与现有方法相比的遗憾，即使查询不完美。 AI
影响 通过将用户对话信号纳入老虎机算法来增强个性化决策。
排序理由 该集群包含一篇详细介绍多目标老虎机新算法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and unknown preferences, existing methods infer prefe…
Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and unknown preferences, existing methods infer preferences only from utility feedback, entangling pref…