In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages. The great needs of developing intelligent interactive recommendation systems are indicated, which could sequentially suggest users the most proper items by accurately predicting their preferences, while receiving the up-to-date feedback to refine the recommendation results, continuously. Multi-armed bandit algorithms, which have been widely applied into various online systems, are quite capable of delivering such efficient recommendation services. However, few existing bandit models are able to adapt to new changes introduced by the modern recommender systems.

中文翻译：

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Bandit 算法在智能交互推荐系统中的应用

在当今的商业市场中，许多高科技互联网企业不断探索创新的方式来提供最佳的在线用户体验，以获得竞争优势。表明开发智能交互推荐系统的巨大需求，它可以通过准确预测用户的偏好来依次向用户推荐最合适的项目，同时接收最新的反馈以不断改进推荐结果。已广泛应用于各种在线系统的多臂老虎机算法非常有能力提供如此高效的推荐服务。然而，很少有现有的老虎机模型能够适应现代推荐系统引入的新变化。