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tcc2vec: RFM-informed representation learning on call graphs for churn prediction · Study

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Articletcc2vec: RFM-informed representation learning on call graphs for churn prediction

Sandra Mitrović2019

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Articlegreen2019

tcc2vec: RFM-informed representation learning on call graphs for churn prediction

Sandra Mitrović · Bart Baesens · Wilfried Lemahieu · Jochen De Weerdt

23 citations·0/0 files stored·Information Sciences

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Customer churn and segmentation Complex Network Analysis Techniques Advanced Graph Neural Networks

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References

2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change 2008Classifying the segmentation of customer value via RFM model and RS theory 2011Customer churn prediction in telecommunications 2008Social ties and their relevance to churn in mobile telecom networks 2007Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression 2014Improved churn prediction in telecommunication industry using data mining techniques 2010Predicting customer churn in mobile networks through analysis of social groups 2012A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

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2019Churn modeling with probabilistic meta paths-based representation learning 2025A comprehensive survey on customer churn analysis studies

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Year2019

Typearticle

Published2019-02-20

VenueInformation Sciences

ISSN-L0020-0255

Languageen

Citations23

Files0 public / 0

ShelfCustomer churn and segmentation

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doi10.1016/j.ins.2019.02.044

openalex_workW2916634624

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