Pregled bibliografske jedinice broj: 1258880
Churn prediction methods based on mutual customer interdependence
Churn prediction methods based on mutual customer interdependence // Journal of Computational Science, 67 (2023), 101940, 10 doi:10.1016/j.jocs.2022.101940 Izvorni jezik rada (na kojem je rad napisan): (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1258880 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Churn prediction methods based on mutual customer interdependence
Autori
Ljubičić, Karmela ; Merćep, Andro ; Kostanjčar, Zvonko
Izvornik
Journal of Computational Science (1877-7503) 67
(2023);
101940, 10
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Churn prediction ; Machine learning ; Node representation learning ; Graph neural network
Sažetak
Most widespread churn prediction models assume customer independence, ignoring the well-documented propagation of churn influence in a customer network. Although this customer interdependence can be modelled by social network analysis and shallow node representation learning algorithms, these methods are too inefficient and impractical for use in large corporate systems. An efficient solution that incorporates both customer features and interconnections is a graph neural network ; however, its potential for churn prediction is still understudied. This paper provides an overview of the existing approaches and outlines the properties of graph neural networks that make them a promising end-to-end solution.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2019-04-5241 - Algoritmi dubokog podržanog učenja za upravljanje rizicima (DREAM) (Kostanjčar, Zvonko, HRZZ ) ( CroRIS)
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus