Smart prediction of the complaint hotspot problem in mobile network

In telecom communication network,a hot customer complaint often affects hundreds even thousands of users’ service and leads to significant economic losses and bulk complaints.An approach was proposed to predict a customer complaint based on real-time user signaling data.Through analyzing the network...

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Main Authors: Lin ZHU, Juan ZHAO, Yiting WANG, Junlan FENG, Gchao DEN
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2019-05-01
Series:Dianxin kexue
Subjects:
Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019099/
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author Lin ZHU
Juan ZHAO
Yiting WANG
Junlan FENG
Gchao DEN
author_facet Lin ZHU
Juan ZHAO
Yiting WANG
Junlan FENG
Gchao DEN
author_sort Lin ZHU
collection DOAJ
description In telecom communication network,a hot customer complaint often affects hundreds even thousands of users’ service and leads to significant economic losses and bulk complaints.An approach was proposed to predict a customer complaint based on real-time user signaling data.Through analyzing the network business layer logic,30 key segments related to the user experience in the S1 interface data were selected.Further,one-hot features,statistical derived features,and differential features were extracted to classify user perceptions in detail.Considering the problems of noise data and unbalanced training samples,LightGBM was chosen to train the prediction model.Experiments are conducted to prove the effectiveness and efficiency of this proposal.As of today,this approach has been deployed in our daily business to locate the hot complaint problem scope as well as to report affected users and area.
format Article
id doaj-art-b8c8c018237a4ae7a0ca7cc41cbe9f07
institution Kabale University
issn 1000-0801
language zho
publishDate 2019-05-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-b8c8c018237a4ae7a0ca7cc41cbe9f072025-01-15T03:02:52ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-05-0135172459589696Smart prediction of the complaint hotspot problem in mobile networkLin ZHUJuan ZHAOYiting WANGJunlan FENGGchao DENIn telecom communication network,a hot customer complaint often affects hundreds even thousands of users’ service and leads to significant economic losses and bulk complaints.An approach was proposed to predict a customer complaint based on real-time user signaling data.Through analyzing the network business layer logic,30 key segments related to the user experience in the S1 interface data were selected.Further,one-hot features,statistical derived features,and differential features were extracted to classify user perceptions in detail.Considering the problems of noise data and unbalanced training samples,LightGBM was chosen to train the prediction model.Experiments are conducted to prove the effectiveness and efficiency of this proposal.As of today,this approach has been deployed in our daily business to locate the hot complaint problem scope as well as to report affected users and area.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019099/prediction of the complaint hotspot problemsignaling datafeature extractionLightGBM classifier
spellingShingle Lin ZHU
Juan ZHAO
Yiting WANG
Junlan FENG
Gchao DEN
Smart prediction of the complaint hotspot problem in mobile network
Dianxin kexue
prediction of the complaint hotspot problem
signaling data
feature extraction
LightGBM classifier
title Smart prediction of the complaint hotspot problem in mobile network
title_full Smart prediction of the complaint hotspot problem in mobile network
title_fullStr Smart prediction of the complaint hotspot problem in mobile network
title_full_unstemmed Smart prediction of the complaint hotspot problem in mobile network
title_short Smart prediction of the complaint hotspot problem in mobile network
title_sort smart prediction of the complaint hotspot problem in mobile network
topic prediction of the complaint hotspot problem
signaling data
feature extraction
LightGBM classifier
url http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019099/
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AT yitingwang smartpredictionofthecomplainthotspotprobleminmobilenetwork
AT junlanfeng smartpredictionofthecomplainthotspotprobleminmobilenetwork
AT gchaoden smartpredictionofthecomplainthotspotprobleminmobilenetwork