Influential Billboard Slot Selection using Pruned Submodularity Graph
Dildar Ali, Suman Banerjee, Yamuna Prasad

TL;DR
This paper addresses selecting the most influential billboard slots to maximize advertising impact, proposing a scalable solution using pruned submodularity graphs under the influence model.
Contribution
It introduces a novel approach using pruned submodularity graphs for efficient influence maximization in billboard slot selection.
Findings
Proposes a scalable algorithm for influence maximization.
Effectively handles large billboard datasets.
Achieves near-optimal influence coverage.
Abstract
Billboard Advertisement has emerged as an effective out-of-home advertisement technique and adopted by many commercial houses. In this case, the billboards are owned by some companies and they are provided to the commercial houses slot\mbox{-}wise on a payment basis. Now, given the database of billboards along with their slot information which slots should be chosen to maximize the influence. Formally, we call this problem as the \textsc{Influential Billboard Slot Selection} Problem. In this paper, we pose this problem as a combinatorial optimization problem. Under the `triggering model of influence', the influence function is non-negative, monotone, and submodular. However, as the incremental greedy approach for submodular function maximization does not scale well along with the size of the problem instances, there is a need to develop efficient solution methodologies for this…
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Taxonomy
TopicsAdvanced Graph Theory Research · Consumer Market Behavior and Pricing · Complexity and Algorithms in Graphs
