Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data

doi:10.12142/ZTECOM.202102005

ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 29-43.DOI: 10.12142/ZTECOM.202102005

收稿日期:2021-03-11 出版日期:2021-06-25 发布日期:2021-07-27

Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data

LOU Kaihao¹(), YANG Yongjian¹, YANG Funing¹, ZHANG Xingliang²

^1.Jilin University, Changchun 130012, China
^2.China Mobile Group Jilin Co. , Ltd. , Changchun 130021, China

Received:2021-03-11 Online:2021-06-25 Published:2021-07-27
About author:LOU Kaihao (loukh20@mails.jlu.edu.cn) received the B.E. degree in software engineering from Jilin University, China in 2017, M.S. degree in computer science and technology from Jilin University in 2020. He is currently pursuing the Ph.D. degree in computer science and technology at Jilin University. His current research focuses on mobile crowdsensing and multi-agent reinforcement learning.|YANG Yongjian received his B.E. degree in automatization from Jilin University of Technology, China in 1983, M.E. degree in computer communication from Beijing University of Post and Telecommunications, China 1991, and Ph.D. in software and theory of computer from Jilin University, China in 2005. He is currently a professor and a Ph.D. supervisor at Jilin University, the Vice Dean of the Software College of Jilin University, Director of Key lab under the Ministry of Information Industry, Standing Director of the Communication Academy, and a member of the Computer Science Academy of Jilin Province. His research interests include network intelligence management, wireless mobile communication and services, and wireless mobile communications.|YANG Funing received her B.E. degree in software engineering from Jilin University, China in 2010 and master’s degree from the school of computer science, Beijing University of Posts and Telecommunications, China in 2013. She is currently a teacher at Jilin University, China. Her current research interests include management, mobile crowdsensing, integration and mining of massive traffic data|ZHANG Xingliang received his B.E. degree in software engineering from Jilin University, China in 2010. He is currently an engineer at the Network Management Center of China Mobile Group Jilin Co., Ltd. His current research interests include mobile communication data analysis and big data analysis of 5G.
Supported by:
Jilin Science and Technology Department Key Technology Project(20190304127YY);the National Natural Science Foundations of China(1772230);Natural Science Foundations of Jilin Province(20190201022JC);National Science Key Lab Fund Project(61421010418);Innovation Capacity Building Project of Jilin Province Development and Reform Commission(2020C017-2);Changchun Science and Technology Development Project(18DY005);Key Laboratory of Defense Science and Technology Foundations(61421010418);Jilin Province Young Talents Lifting Projec(3D4196993421);A conference version of the paper appeared in Proceedings of SocialSec^[2]

摘要/Abstract

Abstract:

Out-door billboard advertising plays an important role in attracting potential customers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour distance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract potential customers’ implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard selection problem is formulated as the probabilistic set coverage problem. We propose two heuristic advertising strategies to greedily select advertising billboards, which achieves the expected maximum commercial profit with the lowest cost. When the advertiser has multiple products, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experiments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.

Key words: billboard advertising, mobile Crowdsensing, probabilistic set coverage problem, simulated annealing, optimization problem

. [J]. ZTE Communications, 2021, 19(2): 29-43.

LOU Kaihao, YANG Yongjian, YANG Funing, ZHANG Xingliang. Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data[J]. ZTE Communications, 2021, 19(2): 29-43.

图/表 14

Figure 1 Problem description of billboard advertising, where the advertiser has only one kind of product

Figure 2 Problem description of billboard advertising, when the advertiser has multiple products

Table 1 An example for removing and inserting billboards

	Billboard A	Billboard B
Advertisement 1	200	300
Advertisement 2	300	200
Advertisement 3	400	100

Figure 3 Performances on the roma/taxi trace set, when all billboards have the same cost

Figure 4 Performances on the epfl trace set, when all billboards have the same cost

Figure 5 Performances on the geolife trace set, when all billboards have the same cost

Table 2 Simulation results on epfl, when all billboards have the same cost

Algorithm	Deadline
Algorithm	500	510	520	530	540	550
Optimal	37.06	37.27	37.71	37.85	37.87	37.91
$1 - 1 e ?$ Optimal	23.42	23.56	23.84	23.92	23.94	23.96
ASFCC	35.34	35.39	35.42	35.48	35.52	35.54

Table 2 Simulation results on epfl, when all billboards have the same cost

Algorithm	Deadline
Algorithm	500	510	520	530	540	550
Optimal	37.06	37.27	37.71	37.85	37.87	37.91
$1 - 1 e ?$ Optimal	23.42	23.56	23.84	23.92	23.94	23.96
ASFCC	35.34	35.39	35.42	35.48	35.52	35.54

Figure 6 Performances on the roma/taxi trace set, when all billboards have different costs

Figure 7 Performances on the epfl trace set, when all billboards have different costs

Figure 8 Performances on the geolife trace set, when all billboards have different costs

Table 3 Simulation results on epfl, when all billboards have different costs

Algorithm	Deadline
Algorithm	500	510	520	530	540	550
Optimal	37.38	37.41	37.59	37.62	38.02	38.71
$(1 - 1 e) ?$ Optimal	23.63	23.65	23.76	23.78	24.03	24.47
ASFCC	35.37	35.40	35.44	35.45	35.47	35.54

Table 3 Simulation results on epfl, when all billboards have different costs

Algorithm	Deadline
Algorithm	500	510	520	530	540	550
Optimal	37.38	37.41	37.59	37.62	38.02	38.71
$(1 - 1 e) ?$ Optimal	23.63	23.65	23.76	23.78	24.03	24.47
ASFCC	35.37	35.40	35.44	35.45	35.47	35.54

Figure 9 Performances on the roma/taxi trace set, when the advertiser has multiple products

Figure 10 Performances on the epfl trace set, when the advertiser has multiple products

Figure 11 Performances on the geolife trace set, when the advertiser has multiple products

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Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data

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