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

doi:10.1109/BigData50022.2020.9378436

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

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5783-5785
Number of pages	3
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378436
Publication status	Published - 2020 Dec 10
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: 2020 Dec 10 → 2020 Dec 13

Publication series

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

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	20/12/10 → 20/12/13

Keywords

Computational advertisement
advertisement recommendation
digital annealer
real-time bidding

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData50022.2020.9378436

Cite this

Mo, F., Jiao, H., Morisawa, S., Nakamura, M., Kimura, K., Fujisawa, H., Ohtsuka, M., & Yamana, H. (2020). Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 5783-5785). Article 9378436 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378436

Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine. / Mo, Fan; Jiao, Huida; Morisawa, Shun et al.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / 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. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5783-5785 9378436 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mo, F, Jiao, H, Morisawa, S, Nakamura, M, Kimura, K, Fujisawa, H, Ohtsuka, M & Yamana, H 2020, Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378436, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 5783-5785, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 20/12/10. https://doi.org/10.1109/BigData50022.2020.9378436

Mo F, Jiao H, Morisawa S, Nakamura M, Kimura K, Fujisawa H et al. Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 5783-5785. 9378436. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378436

Mo, Fan ; Jiao, Huida ; Morisawa, Shun et al. / Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / 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. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 5783-5785 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

@inproceedings{dff60c9b0aa44880bc7bb8a3d0efc930,

title = "Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine",

abstract = "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.",

keywords = "Computational advertisement, advertisement recommendation, digital annealer, real-time bidding",

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

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",

year = "2020",

month = dec,

day = "10",

doi = "10.1109/BigData50022.2020.9378436",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5783--5785",

editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

}

TY - GEN

T1 - Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine

AU - Mo, Fan

AU - Jiao, Huida

AU - Morisawa, Shun

AU - Nakamura, Makoto

AU - Kimura, Koichi

AU - Fujisawa, Hisanori

AU - Ohtsuka, Masafumi

AU - Yamana, Hayato

PY - 2020/12/10

Y1 - 2020/12/10

N2 - 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.

AB - 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.

KW - Computational advertisement

KW - advertisement recommendation

KW - digital annealer

KW - real-time bidding

UR - http://www.scopus.com/inward/record.url?scp=85103826216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85103826216&partnerID=8YFLogxK

U2 - 10.1109/BigData50022.2020.9378436

DO - 10.1109/BigData50022.2020.9378436

M3 - Conference contribution

AN - SCOPUS:85103826216

T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

SP - 5783

EP - 5785

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

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

A2 - Saltz, Jeff

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Big Data, Big Data 2020

Y2 - 10 December 2020 through 13 December 2020

ER -

Real-Time Periodic Advertisement Recommendation Optimization using Ising Machine

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this