Predicting User Pay Conversion Intention Based on Stacking Ensemble Learning:Case Study of Free Value-Added Games
[Objective]This paper proposes a model based on the Stacking ensemble learning method to predict users'intention to convert to paid services,aiming to identify potential paying users accurately.[Methods]We constructed a model for predicting payment intention based on Stacking ensemble learning.First,we determined the base model combination by their prediction performance.Then,we examined the proposed model performance and portability with game players'behavior data set.[Results]The prediction accuracy of our model reached 90.88％,with a Fl value of 90.71％and an AUC value of 0.960 2.Compared to the Bayesian model with the worst performance,our model improved by 4.15％,4.50％,and 0.106 2,respectively.[Limitations]Our model cannot predict whether players will engage in irrational spending.[Conclusions]This study verifies the applicability of the Stacking ensemble learning method in game payment scenarios.The fusion of multiple models can obtain stable and accurate prediction results of payment intention.The proposed model could predict users'payment intentions in different fields.
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