標題: Time-Dependent Smart Data Pricing Based on Machine Learning
作者: Tsai, Yi-Chia
Cheng, Yu-Da
Wu, Cheng-Wei
Lai, Yueh-Ting
Hu, Wan-Hsun
Jeng, Jeu-Yih
Tseng, Yu-Chee
資訊工程學系
Department of Computer Science
關鍵字: K-nearest neighbor;Machine learning;Network congestion management;Smart data pricing
公開日期: 1-一月-2017
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.
URI: http://dx.doi.org/10.1007/978-3-319-57351-9_14
http://hdl.handle.net/11536/146982
ISSN: 0302-9743
DOI: 10.1007/978-3-319-57351-9_14
期刊: ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2017
Volume: 10233
起始頁: 103
結束頁: 108
顯示於類別:會議論文