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Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

Authors:

Randall A. BerryAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 3, Issue 1

Article No.: 6, Pages 1 - 35

https://doi.org/10.1145/3322205.3311077

Published: 26 March 2019 Publication History

Abstract

Vehicle service providers can display commercial ads in their vehicles based on passengers' origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider's vehicle assignment and pricing decisions for different arcs. For example, the provider's price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. To tackle the problem, we show that the traffic network corresponds to an electrical network. When the effective resistance between two locations is small, there are many paths between the two locations and the provider can easily route vehicles between them. We characterize the dependence of an arc's optimal price on any other arc's ad revenue using the effective resistances between these two arcs' origins and destinations. Furthermore, we study the provider's optimal selection of advertisers when it can only display ads for a limited number of advertisers. If each advertiser has one target arc for advertising, the provider should display ads for the advertiser whose target arc has a small effective resistance. We investigate the performance of our advertiser selection strategy based on a real-world dataset.

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Cited By

Yu LZhang SMeng YDu SChen YRen YZhu H(2024)Privacy-Preserving Location-Based Advertising via Longitudinal Geo-IndistinguishabilityIEEE Transactions on Mobile Computing10.1109/TMC.2023.334813623:8(8256-8273)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3348136
Viktoratos ITsadiras A(2021)Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design FrameworkInformation10.3390/info1211048012:11(480)Online publication date: 19-Nov-2021
https://doi.org/10.3390/info12110480
Ma WXu H(2021)A Study of the Partnership Between Advertisers and PublishersPassive and Active Measurement10.1007/978-3-030-72582-2_33(564-580)Online publication date: 29-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72582-2_33
Show More Cited By

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Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 3, Issue 1

March 2019

600 pages

EISSN:2476-1249

DOI:10.1145/3322205

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2019

Published in POMACS Volume 3, Issue 1

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Cited By

Yu LZhang SMeng YDu SChen YRen YZhu H(2024)Privacy-Preserving Location-Based Advertising via Longitudinal Geo-IndistinguishabilityIEEE Transactions on Mobile Computing10.1109/TMC.2023.334813623:8(8256-8273)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3348136
Viktoratos ITsadiras A(2021)Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design FrameworkInformation10.3390/info1211048012:11(480)Online publication date: 19-Nov-2021
https://doi.org/10.3390/info12110480
Ma WXu H(2021)A Study of the Partnership Between Advertisers and PublishersPassive and Active Measurement10.1007/978-3-030-72582-2_33(564-580)Online publication date: 29-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72582-2_33
Yu HWei EBerry RMelodia TEkici EAbouzeid AChen M(2020)Learning to price vehicle service with unknown demandProceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3397166.3409129(161-170)Online publication date: 11-Oct-2020
https://dl.acm.org/doi/10.1145/3397166.3409129

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