A deep RL architecture using imitation learning and reinforcement learning is proposed to model immediate and future values of search story recommendations in a Markov decision process framework.
Specifically, the controller is a multi-head neural network, which is used as the function approximator for choosing the best story from the story embedding pool
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Deep Reinforcement Learning for Personalized Search Story Recommendation
A deep RL architecture using imitation learning and reinforcement learning is proposed to model immediate and future values of search story recommendations in a Markov decision process framework.