这篇论文用Ret-DNN加XGBoost预测电商客户行为，MAE仅0.2193，比单独用Ret-DNN效果更好，适合做电商预测研究的人参考。
该研究针对电商平台难以预测客户未来购买行为的问题，提出混合Ret-DNN（零售深度神经网络）与XGBoost（极端梯度提升）模型。数据来自英国在线零售商，包含近500,000条交易记录。Ret-DNN作为特征提取器捕获时间特征，XGBoost基于提取特征输出购买概率。该模型在测试集上达到0.2193的平均绝对误差（MAE），优于纯Ret-DNN模型。
Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model
In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To overcome these challenges, this study proposes a hybrid Retail Deep NeuralNetwork (Ret-DNN) with an Extreme Gradient Boosting(XGBoost) model for capturing temporal features and tabular dynamics of retail data. First, data were sourced from a UnitedKingdom (UK)-based online retailer that contains transactions with almost 500,000 records. Then, the collected data were pre-processed using a series of techniques, such as data cleaning, outlier handling, temporal feature extraction, feature encoding, and z-score normalization, to ensure that the data were ready for model training and testing. Subsequently, the preprocessed data were fed into the Ret-DNN model, which acts as a feature extractor to understand the complete context of customer transactions. Further, the extracted data were fed as input into the XGBoost model, which predicted the final output as the purchase probability of customers. Finally, the proposed Ret-DNN XGBoost model achieved better results by attaining aMean Absolute Error (MAE) 0.2193 when compared to the existing Ret-DNN model. Keywords: Customer behavior forecasting, extreme gradientboosting, electronic commerce, predictive analytic, retail deepneural networks.