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 |