用混合模型预测电商用户购买行为，准确率优于传统方法。
Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model
- 结合Ret-DNN与XGBoost，分别捕捉时序特征和表格数据动态。
- 在近50万条英国电商数据上，预测误差MAE降至0.2193。
- 适合电商平台做用户行为分析与精准营销的从业者参考。
近年来，电子（E） commerce服务已深度融入人们日常生活，使在线购物成为常态。然而，零售平台难以理解客户行为，导致未来购买预测困难。为解决这一问题，本文提出一种混合零售深度神经网络（Ret-DNN）与极端梯度提升（XGBoost）模型的方法，以捕捉零售数据的时序特征与表格动态。数据来源于一家英国在线零售商，包含近50万条交易记录。通过数据清洗、异常值处理、时间特征提取、特征编码及z-score标准化等预处理步骤，确保数据适用于模型训练与测试。随后，预处理数据输入Ret-DNN模型作为特征提取器，全面理解客户交易上下文；再将提取的特征输入XGBoost模型，预测客户的购买概率。最终，所提模型在均方误差（MAE）上达到0.2193，优于现有Ret-DNN模型。
原文摘要 · Abstract (English)
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.
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