Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine

Fan Mo, Huida Jiao, Shun Morisawa, Makoto Nakamura, Koichi Kimura, Hisanori Fujisawa, Masafumi Ohtsuka, Hayato Yamana

研究成果

抄録

Online advertising is widely used by commercial companies to attract customers. Tuning advertisement delivery to achieve a high conversion rate (CVR) is crucial for improving advertising effectiveness. Because advertisers require demandside platforms (DSPs) to deliver a certain number of ads within a fixed period, it is challenging to maximize CVR while satisfying ads delivery constraints. Such a combinatorial optimization problem is NP-hard when we have a considerable number of both ads and users. In this paper, we adopt Digital Annealer (DA), a quantum-inspired Ising computer, to solve the combinatorial optimization problem. The experimental evaluation result shows that the proposed method increases accuracy from 0.176 to 0.326 and achieves 20.8 times speed-up compared to baseline.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
編集者Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5783-5785
ページ数3
ISBN(電子版)9781728162515
DOI
出版ステータスPublished - 2020 12 10
イベント8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
継続期間: 2020 12 102020 12 13

出版物シリーズ

名前Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
国/地域United States
CityVirtual, Atlanta
Period20/12/1020/12/13

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理

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