Non-zero-sum stackelberg budget allocation game for computational advertising

Daisuke Hatano, Yuko Kuroki, Yasushi Kawase, Hanna Sumita, Naonori Kakimura, Ken Ichi Kawarabayashi

Research output: Contribution to journalArticlepeer-review

Abstract

Computational advertising has been studied to design efficientmarketing 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 al- location 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.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2019 Jun 13

ASJC Scopus subject areas

  • General

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