Efficient and Effective Algorithms for Revenue Maximization in Social Advertising

Kai Han, Benwei Wu, Jing Tang, Shuang Cui, Cigdem Aslay, Laks VS Lakshmanan

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9 Citationer (Scopus)

Abstract

We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owner gains from the advertisers by propagating their ads in the network is maximized. Previous studies on this problem show that it is intractable and present approximation algorithms. We revisit this problem from a fresh perspective and develop novel efficient approximation algorithms, both under the setting where an exact influence oracle is assumed and under one where this assumption is relaxed. Our approximation ratios significantly improve upon the previous ones. Furthermore, we empirically show, using extensive experiments on four datasets, that our algorithms considerably outperform the existing methods on both the solution quality and computation efficiency.

OriginalsprogEngelsk
TitelProceedings of the 2021 ACM SIGMOD International Conference on Management of Data
Antal sider14
UdgivelsesstedNew York
ForlagAssociation for Computing Machinery
Publikationsdato2021
Sider671-684
ISBN (Elektronisk)978-1-4503-8343-1
DOI
StatusUdgivet - 2021
Begivenhed2021 ACM SIGMOD International Conference on Management of Data - Xi'an , Kina
Varighed: 20 jun. 202125 jun. 2021
https://2021.sigmod.org/

Konference

Konference2021 ACM SIGMOD International Conference on Management of Data
Land/OmrådeKina
ByXi'an
Periode20/06/202125/06/2021
Internetadresse

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