TY - GEN
T1 - Maximizing the utility in location-based mobile advertising
AU - Cheng, Peng
AU - Lian, Xiang
AU - Chen, Lei
AU - Liu, Siyuan
N1 - Funding Information:
VI. ACKNOWLEDGMENT Peng Cheng and Lei Chen are partially supported by the Hong Kong RGC GRF Project 16207617, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Hong Kong ITC ITF grants ITS/391/15FX and ITS/212/16FP, Didi-HKUST joint research lab project, Microsoft Research Asia Collaborative Research Grant and Wechat Research Grant. Xiang Lian is partially supported by NSF OAC No. 1739491 and Lian Start Up No. 220981, Kent State University. Siyuan Liu is partially supported by Natural Science Foundation of China: 61572488 and 61673241. Peng Cheng is the corresponding author.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
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.
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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
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
Y2 - 8 April 2019 through 11 April 2019
ER -