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Open AccessArticle

Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments

by 1,2,†, 3,*,†, 1, 1, 4 and 5
1
School of Computer, Hangzhou Dianzi University, Hangzhou 310018, China
2
Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310018, China
3
School of Software, Xidian University, Xi’an 710126, China
4
Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, China
5
Fushun Power Supply Branch, State Grid Liaoning Electric Power Supply Co., Ltd., Fushun 113008, China
*
Author to whom correspondence should be addressed.
The two authors Yuyu Yin and Yueshen Xu contribute equally to this paper, and they are co-first authors.
Entropy 2017, 19(7), 358; https://doi.org/10.3390/e19070358
Received: 18 May 2017 / Revised: 4 July 2017 / Accepted: 10 July 2017 / Published: 20 July 2017
(This article belongs to the Special Issue Information Theory and 5G Technologies)
Mobile Service selection is an important but challenging problem in service and mobile computing. Quality of service (QoS) predication is a critical step in service selection in 5G network environments. The traditional methods, such as collaborative filtering (CF), suffer from a series of defects, such as failing to handle data sparsity. In mobile network environments, the abnormal QoS data are likely to result in inferior prediction accuracy. Unfortunately, these problems have not attracted enough attention, especially in a mixed mobile network environment with different network configurations, generations, or types. An ensemble learning method for predicting missing QoS in 5G network environments is proposed in this paper. There are two key principles: one is the newly proposed similarity computation method for identifying similar neighbors; the other is the extended ensemble learning model for discovering and filtering fake neighbors from the preliminary neighbors set. Moreover, three prediction models are also proposed, two individual models and one combination model. They are used for utilizing the user similar neighbors and servicing similar neighbors, respectively. Experimental results conducted in two real-world datasets show our approaches can produce superior prediction accuracy. View Full-Text
Keywords: Mobile Service Selection; Mobile Network; Ensemble Learning; QoS Prediction; Abnormal QoS Mobile Service Selection; Mobile Network; Ensemble Learning; QoS Prediction; Abnormal QoS
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Yin, Y.; Xu, Y.; Xu, W.; Gao, M.; Yu, L.; Pei, Y. Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments. Entropy 2017, 19, 358.

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