野外场景下老虎机问题的挑战与解决方案：面向跨队列榜单推荐的热启动混合老虎机
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
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中文总结 AI 辅助
针对推荐系统冷启动队列的挑战，提出热启动混合老虎机CohortMix-TS，利用跨队列迁移和库存感知榜单构建，提升早期推荐质量并防止物品耗尽，经模拟和野外部署验证有效。
中文摘要 AI 辅助
许多推荐服务反复遭遇冷启动队列，即新用户几乎没有交互历史。这带来了两个挑战：从有限的反馈中快速学习用户偏好，以及在每个用户拥有有限目录（可能随时间变得重复或耗尽）的情况下维持有用的推荐。我们提出了CohortMix-TS，一种热启动混合老虎机，它从早期队列中学习潜在用户群体，并利用可用元数据为新用户构建群体信息先验。从这些固定先验出发，模型随着每个用户的反馈变得可用而独立地进行个性化。会话榜单结合了汤普森采样与多样性和库存消耗控制。我们通过模拟、半合成实验以及一次为期25天的野外随机部署（在校园游戏问答应用中有713名注册参与者）来评估CohortMix-TS。我们的评估表明，跨队列迁移提高了早期推荐质量和用户级遗憾，而库存感知的榜单构建有助于防止首选物品过早耗尽。在实地部署中，治疗组用户在正确性上的早期到晚期变化也大于接受随机推荐的用户。这些结果共同表明，热启动迁移和库存感知推荐如何支持短期、反复冷启动队列的个性化。
英文摘要
Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferences quickly from limited feedback and sustaining useful recommendations when each user has a finite catalog that can become repetitive or depleted over time. We propose CohortMix-TS, a warm-started mixture bandit that learns latent user groups from earlier cohorts and uses available metadata to construct group-informed priors for new users. Starting from these fixed priors, the model personalizes independently as feedback from each user becomes available. Session slates combine Thompson sampling with diversity and inventory-depletion controls. We evaluate CohortMix-TS through simulation, semi-synthetic experiments, and a 25-day randomized in-the-wild deployment with 713 registered participants in a Campus Games quiz application. Our evaluations show that cross-cohort transfer improves early recommendation quality and user-level regret, while inventory-aware slate construction helps prevent premature exhaustion of preferred items. In the field deployment, treatment users also showed a larger early-to-late change in correctness than users receiving random recommendations. Together, these results show how warm-start transfer and inventory-aware recommendations can support personalization for short-lived, repeatedly cold-starting cohorts.
发表机构
- German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
- RPTU（莱茵兰-普法尔茨州凯泽斯劳滕-兰道工业大学（RPTU））
- LMU（慕尼黑大学（LMU））
机构由 AI 辅助整理，请以论文原文为准。
