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A Context Aware Personalized Media Recommendation System: An Adaptive Evolutionary Algorithm Approach

Smart devices and pervasive connectivity have lead to an increase in demand for providing intelligent personalized services that improve user experience. Providing personalized media recommendation services that recommend content relevant to the user is gaining prevalence. In this paper we investigate the use of context capture on the user's devices as a method of learning all the user activity patterns and using these patterns to generate content recommendations. We propose a novel recommendation mechanism based on an evolutionary algorithm that evaluates new content based on multiple objectives. We show by way of simulation, improvements in the provided recommendations compared to traditional methods.