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Deep trajectory: a deep learning approach for mobile advertising in vehicular networks

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Abstract

Mobile advertising is an interesting research topic and a promising commercial application in vehicular networks, which is expected to spread the timely information into the network at a lower cost. Seed vehicles are of vital importance for information broadcasting in mobile advertising. How to find the best set of seed vehicles with maximal influence on the network is a very challenging problem. In this paper, we propose developing deep learning schemes to select the most suitable candidate vehicles for mobile advertising so as to achieve maximum coverage and impact in vehicular networks. Specifically, we adopt the deep structure for the mobile advertising problem to exploit the spatial-temporal regularities of vehicle traces. The adaptive models MLP-W and MLP-T are able to either infer the future topology of vehicles explicitly or predict the future trajectory and infer topology implicitly. Then, vehicles with higher centralities are considered as good seeds for timely information disseminating. Extensive experiments have been conducted over two real-world taxi trace datasets. And the experimental results demonstrate the efficacy of our proposed methodology compared with the state-of-the-art approach for mobile advertising tasks.

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Notes

  1. Note that the “familiar stranger pattern” will be more obvious if we include the bus trace data.

  2. As the max derivative value for a Sigmoid activation function \(\sigma (x)\) will be 1/4. So when implementing BP, we have \(\left| w_j*\sigma '(x_j)\right| <1/4\), where \(w_j\) is the weights for very layer, \(\sigma '(x_j)\) is the derivative of Sigmoid activation function. So there will be a product of many such terms, and the product will tend to exponentially decrease: the deeper the structure is, the smaller the product will be, which is the so-called vanishing gradient problem in deep learning area. However, ReLU activation function does not have this problem.

  3. The LSTM used in the experiment has two hidden layers with 5000 neurons in each layer, the dropout value is 0.5, the activation function used in the hidden layers is ReLU.

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Acknowledgements

This work has been partially supported by National Key R&D Program of China under Grant No. 2017YFB0803300 and NSFC under Grant No. 61772074.

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Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Xin Li, Liang Yang & Yi Li

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Chuan Zhou

  3. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Chuan Zhou

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  1. Xin Li
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  2. Liang Yang
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  3. Yi Li
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Correspondence to Chuan Zhou.

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Li, X., Yang, L., Li, Y. et al. Deep trajectory: a deep learning approach for mobile advertising in vehicular networks. Neural Comput & Applic 31, 2813–2825 (2019). https://doi.org/10.1007/s00521-017-3231-1

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