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In wireless push systems, the server schedules the broadcasts of its information items aiming at satisfying the clients' preferences efficiently. Latest research efforts have proposed adaptive push systems, enhanced with a learning automaton, in which the server has the ability to update its estimated item demand probability vector. This vector indicates the level of the items' desirability. Even though the adaptive push systems are capable of operating in dynamic environments, where the item demand probability distribution changes periodically, the time that the learning automaton needs to adapt its estimated probability vector to a new demand probability distribution leads to degradation of the system's performance.
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