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.
As illustrated in Figure 2, our dynamic model consists of two units: a reward model MR and a transition model MT
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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.