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A Comprehensive Review of LSTM-Based Churn Prediction Models in the Gaming Industry | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 3، شماره 3 - شماره پیاپی 13، بهمن 2023، صفحه 51-60 اصل مقاله (383.22 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2024.32429.1135 | ||
| نویسندگان | ||
| Fatemeh Vatani1؛ Morteza Dorrigiv* 2؛ Samaneh Emami3 | ||
| 1Electrical and Computer Engineering department, Semnan University, Semnan, Iran. | ||
| 2Faculty of Electrical and Computer Engineering , Semnan University, Semnan, Iran. | ||
| 3Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 03 آذر 1402، تاریخ بازنگری: 23 مرداد 1403، تاریخ پذیرش: 17 دی 1403 | ||
| چکیده | ||
| Client churn is a significant issue affecting companies across various industries. In the gaming sector, customer loss is particularly critical as it directly impacts revenue, profit margins, and customer retention. Inaccurate predictions of client churn can lead to substantial revenue losses. Churn prediction involves identifying customers who are most likely to cancel their subscriptions. This practice has become essential for many modern organizations due to its performance benefits, aiding businesses in calculating revenue growth and client retention metrics. This paper classifies player churn prediction models into seven main categories to comprehensively review the existing literature. This classification enhances the understanding of various methodologies used in the field and highlights potential areas for future research. Notably, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, have demonstrated significant potential among deep learning models. This paper examines the contribution of LSTM networks in predicting churn in computer games. | ||
| کلیدواژهها | ||
| Customer churn؛ Classification؛ Deep neural network؛ Long short-term memory؛ Survival analysis؛ Game data mining | ||
| مراجع | ||
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