Time-dependent smart data pricing based on machine learning

Yi Chia Tsai*, Yu Da Cheng, Cheng Wei Wu, Yueh Ting Lai, Wan Hsun Hu, Jeu Yih Jeng, Yu-Chee Tseng

*Corresponding author for this work

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

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’ 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.

原文English
主出版物標題Advances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Proceedings
編輯Philippe Langlais, Malek Mouhoub
發行者Springer Verlag
頁面103-108
頁數6
ISBN(列印)9783319573502
DOIs
出版狀態Published - 1 一月 2017
事件30th Canadian Conference on Artificial Intelligence, AI 2017 - Edmonton, Canada
持續時間: 16 五月 201719 五月 2017

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10233 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference30th Canadian Conference on Artificial Intelligence, AI 2017
國家Canada
城市Edmonton
期間16/05/1719/05/17

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