An Effective Tag Assignment Approach for Billboard Advertisement

TL;DR

This paper introduces a novel approach for assigning tags to billboard advertisement slots using a specialized bipartite matching model, enhancing advertising relevance and effectiveness.

Contribution

It models the tag assignment as a One-To-Many Bipartite Matching problem and proposes an iterative solution with proven efficiency and effectiveness.

Findings

The proposed method outperforms baseline approaches in real-world datasets.

The iterative solution effectively maximizes influence in tag assignment.

Complexity analysis confirms the approach's scalability.

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.