Abstract
The purpose of t ime-dependent smart d ata p ricing (abbreviated as TDP) is to relieve network congestion by offering network users different prices over varied periods. However, traditional TDP has not considered applying machine learning concepts in determining prices. In this paper, we propose a new framework for TDP based on machine learning concepts. We propose two different pricing algorithms, named TDP-TR (TDP based on T ransition R ules) and TDP-KNN (TDP based on K-N earest N eighbors). TDP-TR determines prices based on users’ past willingness to pay given different prices, while TDP-KNN determines prices based on the similarity of users’ past network usages. The main merit of TDP-TR is low computational cost, while that of TDP-KNN is low maintenance cost. Experimental results on simulated datasets show that the proposed algorithms have good performance and profitability.
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Tsai, YC. et al. (2017). Time-Dependent Smart Data Pricing Based on Machine Learning. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_14
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DOI: https://doi.org/10.1007/978-3-319-57351-9_14
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