Search, recommendation, and advertising are the three most important information-providing mechanisms. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem on the Web. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques. These DRL based techniques have two key advantages -- (1) they are able to continuously update information seeking strategies according to users' real-time feedback, and (2) they can maximize the expected cumulative long-term reward from users where reward has different definitions according to information seeking applications such as click-through rate, revenue, user satisfaction and engagement. In this survey, we give an overview about deep reinforcement learning for search, recommendations, and advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.
翻译:信息搜索、建议和广告是三大信息提供机制。这些信息搜索技术,通过在适当时间和地点建议用户个性化物品(信息或服务)满足用户的信息需求,在缓解网上信息超载问题方面发挥着关键作用。随着在深层强化学习(DRL)方面最近取得的巨大进展,人们越来越有兴趣开发基于DRL的信息搜索技术。这些基于DRL的技术有两个主要优势:(1) 他们能够不断更新信息,根据用户实时反馈寻求战略;(2) 他们可以最大限度地增加预期从用户获得的长期奖励,因为根据点击率、收入、用户满意度和参与等寻求应用的信息,对用户的奖励有不同的定义。在这次调查中,我们概述了从应用方法到应用的深度强化学习、建议、广告、从方法到应用的广告、审查有代表性的算法以及讨论一些有吸引力的研究方向。