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A distributed high efficiency similarity matrix computation method based on users’ mobile network access location
- Telecommunications Science Vol. 34, Issue 5, Pages: 26-38(2018)
- 作者机构：
1. 武汉大学电子信息学院，湖北 武汉 430072
2. 武汉船舶通信研究所，湖北 武汉 430070
3. 武汉技师学院，湖北 武汉 430051
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.1000-0801.2018136
CLC： TP393-
Published Online：2018-05，
Published：20 May 2018
- 稿件说明：
移动端阅览
Yuan WANG, Hao JIANG, Ming WU, et al. A distributed high efficiency similarity matrix computation method based on users’ mobile network access location[J]. Telecommunications science, 2018, 34(5): 26-38.
Yuan WANG, Hao JIANG, Ming WU, et al. A distributed high efficiency similarity matrix computation method based on users’ mobile network access location[J]. Telecommunications science, 2018, 34(5): 26-38. DOI： 10.11959/j.issn.1000-0801.2018136.
摘要
研究了一种基于用户移动网络接入位置的高效分布式相似矩阵计算方法，利用Hadoop生态系统中的MapReduce 计算框架，依据地理位置信息对用户进行划分并进行相似度计算。实验结果表明，该方法在计算效率上相较于现有相似矩阵计算方法最大提升了近25倍。将相似矩阵计算结果应用于用户社区发现，该方法与现有相似矩阵计算方法得到的相似矩阵有几乎一致的社区发现结果，一致率高达99.9%。
Abstract
A distributed similarity matrix computation method based on users’ mobile network access location was proposed.MapReduce computing framework in the Hadoop ecosystem was used
and users’ mobile network access location information was considered as prior knowledge
then users were divided based on geographic location information and the similarity calculation was performed.The experimental results show that the computational efficiency of the proposed method is increased by nearly 25 times compared with the existing similarity matrix computation method.As for the outcome of the community detection
the proposed method get nearly the same results as the existing similar matrix computation method
the agreement rate is nearly 99.9%.
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references
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