Maximizing the utility in location-based mobile advertising

Peng Cheng, Xiang Lian, Lei Chen, Siyuan Liu

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

Abstract

Nowadays, the locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisement. In this paper, we consider a location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the reconciliation approach, which has an approximation ratio of (1-ϵ)⋅θ, where θ = min(a-1\n^c-1, a-2 n^c-2,..., a-m n^c-m), and n^c-i is the larger value between the number of valid vendors and the capacity a-i of customer u-i. Experiments on real data sets confirm the efficiency and effectiveness of our proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages1626-1629
Number of pages4
ISBN (Electronic)9781538674741
DOIs
StatePublished - Apr 1 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period4/8/194/11/19

Fingerprint

Marketing
Computational complexity
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Cite this

Cheng, P., Lian, X., Chen, L., & Liu, S. (2019). Maximizing the utility in location-based mobile advertising. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 1626-1629). [8731347] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00158
Cheng, Peng ; Lian, Xiang ; Chen, Lei ; Liu, Siyuan. / Maximizing the utility in location-based mobile advertising. Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. pp. 1626-1629 (Proceedings - International Conference on Data Engineering).
@inproceedings{e4e7974df20b4a9e929542a0c22169e1,
title = "Maximizing the utility in location-based mobile advertising",
abstract = "Nowadays, the locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisement. In this paper, we consider a location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the reconciliation approach, which has an approximation ratio of (1-ϵ)⋅θ, where θ = min(a-1\n^c-1, a-2 n^c-2,..., a-m n^c-m), and n^c-i is the larger value between the number of valid vendors and the capacity a-i of customer u-i. Experiments on real data sets confirm the efficiency and effectiveness of our proposed approach.",
author = "Peng Cheng and Xiang Lian and Lei Chen and Siyuan Liu",
year = "2019",
month = "4",
day = "1",
doi = "10.1109/ICDE.2019.00158",
language = "English (US)",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "1626--1629",
booktitle = "Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019",
address = "United States",

}

Cheng, P, Lian, X, Chen, L & Liu, S 2019, Maximizing the utility in location-based mobile advertising. in Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019., 8731347, Proceedings - International Conference on Data Engineering, vol. 2019-April, IEEE Computer Society, pp. 1626-1629, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 4/8/19. https://doi.org/10.1109/ICDE.2019.00158

Maximizing the utility in location-based mobile advertising. / Cheng, Peng; Lian, Xiang; Chen, Lei; Liu, Siyuan.

Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 1626-1629 8731347 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).

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

TY - GEN

T1 - Maximizing the utility in location-based mobile advertising

AU - Cheng, Peng

AU - Lian, Xiang

AU - Chen, Lei

AU - Liu, Siyuan

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Nowadays, the locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisement. In this paper, we consider a location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the reconciliation approach, which has an approximation ratio of (1-ϵ)⋅θ, where θ = min(a-1\n^c-1, a-2 n^c-2,..., a-m n^c-m), and n^c-i is the larger value between the number of valid vendors and the capacity a-i of customer u-i. Experiments on real data sets confirm the efficiency and effectiveness of our proposed approach.

AB - Nowadays, the locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisement. In this paper, we consider a location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the reconciliation approach, which has an approximation ratio of (1-ϵ)⋅θ, where θ = min(a-1\n^c-1, a-2 n^c-2,..., a-m n^c-m), and n^c-i is the larger value between the number of valid vendors and the capacity a-i of customer u-i. Experiments on real data sets confirm the efficiency and effectiveness of our proposed approach.

UR - http://www.scopus.com/inward/record.url?scp=85067989761&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067989761&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2019.00158

DO - 10.1109/ICDE.2019.00158

M3 - Conference contribution

AN - SCOPUS:85067989761

T3 - Proceedings - International Conference on Data Engineering

SP - 1626

EP - 1629

BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019

PB - IEEE Computer Society

ER -

Cheng P, Lian X, Chen L, Liu S. Maximizing the utility in location-based mobile advertising. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society. 2019. p. 1626-1629. 8731347. (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2019.00158