Abstract
We study web advertisements scheduling problem by fully considering the interaction of web users and web advertisements publishing system. We construct a Markov Decision Process (MDP) based web advertisements scheduling model and schedule advertisements publishing during the whole process of web surfing by the users, thus we make maximal use of personal behavior characteristics of every web user in the scheduling model. We also track the user habit with reinforcement learning, solve the MDP model by TD(λ) algorithm combing the function approximator, and obtain adaptive online scheduling policies for web advertisements publishing.
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