Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model
Researchers have developed a hybrid Ret-DNN with XGBoost model to improve customer behavior forecasting in e-commerce. This model combines a deep neural network for feature extraction with gradient boosting for prediction, utilizing data from a UK-based online retailer. The proposed model achieved a Mean Absolute Error of 0.2193, outperforming the existing Ret-DNN model. AI
IMPACT This hybrid model offers improved accuracy for e-commerce platforms seeking to understand and predict customer purchasing behavior.