在智能互动建议系统中使用强盗算法 (The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems)

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.

翻译：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方法,为获取竞争优势提供最佳在线用户经验。指出开发智能互动推荐系统的巨大需求,这可以通过准确预测用户的偏好,连续地向用户建议最合适的项目,同时不断收到最新反馈,以完善建议结果。多武装强盗算法已广泛应用于各种在线系统,非常能够提供这种高效的建议服务。然而,很少有现有强盗模式能够适应现代推荐系统带来的新变化。