Influence Maximization in Trajectory Databases - 2017


We tend to study a novel problem of influence maximization in trajectory databases that's terribly useful in precise locationaware advertising. It finds k best trajectories to be hooked up with a given advertisement and maximizes the expected influence among a giant cluster of audience. We have a tendency to show that the matter is NP-exhausting and propose each actual and approximate solutions to find the most effective set of trajectories. We tend to also extend our drawback to support the state of affairs when there are a group of advertisements. We validate our approach via in depth experiments with real datasets.

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