  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.
