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Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou, K, Yang, Y, Wang, E, Liu, Z, Baker, T and Bashir, AK (2020) Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050

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As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers’ payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers’ implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

Item Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: 0905 Civil Engineering, 1507 Transportation and Freight Services, 0801 Artificial Intelligence and Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers
Date Deposited: 15 Apr 2020 11:02
Last Modified: 04 Sep 2021 07:28
URI: https://researchonline.ljmu.ac.uk/id/eprint/12734
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