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

3 Scopus citations


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
Number of pages4
ISBN (Electronic)9781538674741
StatePublished - Apr 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
ISSN (Print)1084-4627


Conference35th IEEE International Conference on Data Engineering, ICDE 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems


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