@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",
}