Influential Billboard Slot Selection using Pruned Submodularity Graph

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 $k$ 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 problem.