Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey
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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.
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