# Non-zero-sum Stackelberg Budget Allocation Game for Computational Advertising

## TL;DR

This paper introduces a novel Stackelberg game model for budget allocation in computational advertising, addressing competitive market dynamics and proposing algorithms with proven guarantees and efficiency.

## Contribution

It formalizes a new Stackelberg budget allocation model with a bipartite influence structure and develops algorithms for finding strong equilibria, including approximation, heuristic, and exact methods.

## Key findings

- Algorithms outperform baseline in real-world tests
- Proposed methods effectively handle competitive influence
- Exact algorithm works for disjoint customer cases

## Abstract

Computational advertising has been studied to design efficient marketing strategies that maximize the number of acquired customers. In an increased competitive market, however, a market leader (a leader) requires the acquisition of new customers as well as the retention of her loyal customers because there often exists a competitor (a follower) who tries to attract customers away from the market leader. In this paper, we formalize a new model called the Stackelberg budget allocation game with a bipartite influence model by extending a budget allocation problem over a bipartite graph to a Stackelberg game. To find a strong Stackelberg equilibrium, a standard solution concept of the Stackelberg game, we propose two algorithms: an approximation algorithm with provable guarantees and an efficient heuristic algorithm. In addition, for a special case where customers are disjoint, we propose an exact algorithm based on linear programming. Our experiments using real-world datasets demonstrate that our algorithms outperform a baseline algorithm even when the follower is a powerful competitor.