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Billboard advertising is a popular out-of-home advertising technique adoptedby commercial houses. Companies own billboards and offer them to commercialhouses on a payment basis. Given a database of billboards with slotinformation, we want to determine which k slots to choose to maximizeinfluence. We call this the INFLUENTIAL BILLBOARD SLOT SELECTION (IBSS) Problemand pose it as a combinatorial optimization problem. We show that the influencefunction considered in this paper is non-negative, monotone, and submodular.The incremental greedy approach based on the marginal gain computation leads toa constant factor approximation guarantee. However, this method scales verypoorly when the size of the problem instance is very large. To address this, wepropose a spatial partitioning and pruned submodularity graph-based approachthat is divided into the following three steps: preprocessing, pruning, andselection. We analyze the proposed solution approaches to understand theirtime, space requirement, and performance guarantee. We conduct extensive set ofexperiments with real-world datasets and compare the performance of theproposed solution approaches with the available baseline methods. We observethat the proposed approaches lead to more influence than all the baselinemethods within reasonable computational time.

Influential Billboard Slot Selection using Spatial Clustering and Pruned Submodularity Graph