Before revision:  We use a neural network as the agent to predict the  advertisers' demands based on their profile and historical adoption behaviors. Based on the estimated demand, we apply simulated bidding to derive the optimal bidding strategy for recommendation and interact with the advertiser by displaying the possible advertising performance. To solve the exploration/exploitation dilemma, we use Dropout to represent the uncertainty of the network, which approximately equals to conduct Thompson sampling for efficient strategy exploration.
After revision:  We use a neural network as the agent to predict the advertisers' marketing objectives and the recommending strategies.