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: Chapter in Book/Report/Conference proceedingConference contribution

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 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
Title of host publicationPRICAI 2019
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
PublisherSpringer Verlag
Pages568-582
Number of pages15
ISBN (Print)9783030299071
DOIs
Publication statusPublished - 2019 Jan 1
Event16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
Duration: 2019 Aug 262019 Aug 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11670 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
CountryFiji
CityYanuka Island
Period19/8/2619/8/30

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Keywords

  • Budget allocation problem
  • Stackelberg game
  • Submodular

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hatano, D., Kuroki, Y., Kawase, Y., Sumita, H., Kakimura, N., & Kawarabayashi, K. I. (2019). Non-zero-sum stackelberg budget allocation game for computational advertising. In A. C. Nayak, & A. Sharma (Eds.), PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 568-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11670 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-29908-8_45