Influential Billboard Slot Selection Using Pruned Submodularity Graph

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-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 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.

In this paper, we apply the pruned submodularity graph-based pruning technique for solving this problem. The proposed approach is divided into three phases, namely, preprocessing, pruning, and selection. We analyze the proposed solution approach for its performance guarantee and computational complexity. We conduct an extensive set of experiments with real-world datasets and compare the performance of the proposed solution approach with many baseline methods. We observe that the proposed one leads to more amount of influence compared to all the baseline methods within reasonable computational time.

The work of Dr. Suman Banerjee is supported by the Start Up Research Grant provided by Indian Institute of Technology Jammu, India (Grant No.: SG100047).