Name
Abstract | ||
|---|---|---|
User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into a binary classification task where the users are labeled as churned and non-churned. More recently, some works have tried to convert the user churn prediction problem into the prediction of user return time. In this app... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2020.3000456 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Recurrent neural networks,Predictive models,Task analysis,Microsoft Windows,Analytical models,Computational modeling,History | Journal | 34 |
Issue | ISSN | Citations |
4 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Khodadadi | 1 | 16 | 2.94 |
| Hosseini Seyed Abbas | 2 | 0 | 0.34 |
| Pajouheshgar Ehsan | 3 | 0 | 0.34 |
| Mansouri, Farnam | 4 | 0 | 1.01 |
| Hamid R. Rabiee | 5 | 336 | 41.77 |