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

Fingerprint

Marketing
Customers
Game
Stackelberg Game
Stackelberg Equilibrium
Approximation algorithms
Heuristic algorithms
Solution Concepts
Linear programming
Exact Algorithms
Bipartite Graph
Heuristic algorithm
Approximation Algorithms
Baseline
Disjoint
Efficient Algorithms
Maximise
Advertising
Model
Experiments

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

Non-zero-sum stackelberg budget allocation game for computational advertising. / Hatano, Daisuke; Kuroki, Yuko; Kawase, Yasushi; Sumita, Hanna; Kakimura, Naonori; Kawarabayashi, Ken ichi.

PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. ed. / Abhaya C. Nayak; Alok Sharma. Springer Verlag, 2019. p. 568-582 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11670 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hatano, D, Kuroki, Y, Kawase, Y, Sumita, H, Kakimura, N & Kawarabayashi, KI 2019, Non-zero-sum stackelberg budget allocation game for computational advertising. in AC Nayak & A Sharma (eds), PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11670 LNAI, Springer Verlag, pp. 568-582, 16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019, Yanuka Island, Fiji, 19/8/26. https://doi.org/10.1007/978-3-030-29908-8_45
Hatano D, Kuroki Y, Kawase Y, Sumita H, Kakimura N, Kawarabayashi KI. Non-zero-sum stackelberg budget allocation game for computational advertising. In Nayak AC, Sharma A, editors, PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2019. p. 568-582. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-29908-8_45
Hatano, Daisuke ; Kuroki, Yuko ; Kawase, Yasushi ; Sumita, Hanna ; Kakimura, Naonori ; Kawarabayashi, Ken ichi. / Non-zero-sum stackelberg budget allocation game for computational advertising. PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. editor / Abhaya C. Nayak ; Alok Sharma. Springer Verlag, 2019. pp. 568-582 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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