@inproceedings{4758ea7b97fb43a5a3c8de5fd18d9284,
title = "Time-dependent smart data pricing based on machine learning",
abstract = "The purpose of time-dependent smart data pricing (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 Transition Rules) and TDP-KNN (TDP based on K-Nearest Neighbors). TDP-TR determines prices based on users{\textquoteright} past willingness to pay given different prices, while TDP-KNN determines prices based on the similarity of users{\textquoteright} 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.",
keywords = "K-nearest neighbour, Machine learning, Network congestion management, Smart data pricing",
author = "Tsai, {Yi Chia} and Cheng, {Yu Da} and Wu, {Cheng Wei} and Lai, {Yueh Ting} and Hu, {Wan Hsun} and Jeng, {Jeu Yih} and Yu-Chee Tseng",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-57351-9_14",
language = "English",
isbn = "9783319573502",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "103--108",
editor = "Philippe Langlais and Malek Mouhoub",
booktitle = "Advances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Proceedings",
note = "null ; Conference date: 16-05-2017 Through 19-05-2017",
}