Deep reinforcement learning for search, recommendation, and online advertising: a survey
It addresses the information overload problem for web users by summarizing existing DRL techniques, but it is incremental as it reviews rather than introduces new methods.
This survey provides an overview of deep reinforcement learning (DRL) applications in search, recommendation, and online advertising, highlighting their ability to update strategies based on real-time feedback and maximize long-term rewards like click-through rates and user satisfaction.
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. 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. 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 paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.