An Effective Tag Assignment Approach for Billboard Advertisement
Dildar Ali, Harishchandra Kumar, Suman Banerjee, Yamuna Prasad
TL;DR
The paper tackles the Tag Allocation Problem in Billboard Advertising, where the goal is to assign a subset of tags to a subset of billboard slots to maximize cumulative influence over a trajectory-rich audience. It formulates the problem as a two-sided, one-to-many bipartite matching (OMBM) and proves NP-hardness, then proposes an iterative solution that first selects influential slots and tags via a stochastic greedy method, builds a pruned weighted bipartite graph, and solves the allocation with OMBM. The approach is accompanied by a formal complexity guarantee, with time complexity $O(k^{2} \cdot \ell)$ and space $O(k+\ell)$, and is validated on real-world NYC and VehDS-LA trajectory-billboard datasets, showing superior influence performance compared to baselines. The results demonstrate practical impact for scalable and effective billboard advertising by optimally aligning tag content to audience segments, with potential extensions to consider zonal constraints and multi-advertiser settings.
Abstract
Billboard Advertisement has gained popularity due to its significant outrage in return on investment. To make this advertisement approach more effective, the relevant information about the product needs to be reached to the relevant set of people. This can be achieved if the relevant set of tags can be mapped to the correct slots. Formally, we call this problem the Tag Assignment Problem in Billboard Advertisement. Given trajectory, billboard database, and a set of selected billboard slots and tags, this problem asks to output a mapping of selected tags to the selected slots so that the influence is maximized. We model this as a variant of traditional bipartite matching called One-To-Many Bipartite Matching (OMBM). Unlike traditional bipartite matching, a tag can be assigned to only one slot; in the OMBM, a tag can be assigned to multiple slots while the vice versa can not happen. We propose an iterative solution approach that incrementally allocates the tags to the slots. The proposed methodology has been explained with an illustrated example. A complexity analysis of the proposed solution approach has also been conducted. The experimental results on real-world trajectory and billboard datasets prove our claim on the effectiveness and efficiency of the proposed solution.