Please use this identifier to cite or link to this item:

Title: An Approach to Churn Prediction for Cloud Services Recommendation and User Retention
Authors: Saias, José
Rato, Luis
Gonçalves, Teresa
Editors: Polignano, Marco
Semeraro, Giovanni
Vassilakis, Costas
Keywords: machine learning
decision analysis
Issue Date: 28-Apr-2022
Publisher: Information, MDPI
Citation: Saias, J.; Rato, L.; Gonçalves, T. (2022). An Approach to Churn Prediction for Cloud Services Recommendation and User Retention. Information 2022, 13, 227.
Abstract: The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value.
ISSN: 2078-2489
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

Files in This Item:

File Description SizeFormat
information-13-00227.pdf668.97 kBAdobe PDFView/OpenRestrict Access. You can Request a copy!
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois