动态先验泰勒斯采样让冷启动推荐更精准，避免浪费流量。
Dynamic Prior Thompson Sampling for Cold-Start Exploration in Recommender Systems
- 根据目标竞争概率动态调整先验均值，控制新项目曝光强度。
- 线上实验显示，相比均匀先验，探索效率提升且用户满意度更高。
- 适合大规模推荐系统中冷启动物品的流量分配优化场景。
冷启动探索是大规模推荐系统的核心挑战：新或数据稀疏的项目需获得流量以估计价值，但过度探索会损害用户体验并浪费展示机会。实践中，泰勒斯采样（TS）常以均匀的 Beta(1,1) 先验初始化，隐含假设未见项目的成功率为50%。当真实基线率远低于此，该乐观先验会系统性地高估弱项目。这一影响在批处理策略更新与管道延迟下被放大：数小时内，新项目仍处于‘无数据’状态，先验主导分配，反馈尚未纳入。本文提出动态先验泰勒斯采样（Dynamic Prior Thompson Sampling），通过直接控制新项目击败当前最优项的概率来设计先验。核心贡献是推导出先验均值的闭式二次解，确保引入时 P(X_j > Y_k) = ε，使探索强度可预测、可调节，同时保持 TS 的贝叶斯更新特性。在蒙特卡洛验证、离线批量模拟及一个服务数百万用户的缩略图个性化系统的在线大尺度实验中，动态先验实现了精确的探索控制，并显著优于均匀先验基线。
原文摘要 · Abstract (English)
Cold-start exploration is a core challenge in large-scale recommender systems: new or data-sparse items must receive traffic to estimate value, but over-exploration harms users and wastes impressions. In practice, Thompson Sampling (TS) is often initialized with a uniform Beta(1,1) prior, implicitly assuming a 50% success rate for unseen items. When true base rates are far lower, this optimistic prior systematically over-allocates to weak items. The impact is amplified by batched policy updates and pipeline latency: for hours, newly launched items can remain effectively "no data," so the prior dominates allocation before feedback is incorporated. We propose Dynamic Prior Thompson Sampling, a prior design that directly controls the probability that a new arm outcompetes the incumbent winner. Our key contribution is a closed-form quadratic solution for the prior mean that enforces P(X_j > Y_k) = epsilon at introduction time, making exploration intensity predictable and tunable while preserving TS Bayesian updates. Across Monte Carlo validation, offline batched simulations, and a large-scale online experiment on a thumbnail personalization system serving millions of users, dynamic priors deliver precise exploration control and improved efficiency versus a uniform-prior baseline.
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