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.

翻译：近年来，电子商务服务在人们的日常生活中迅速普及，帮助用户在线购买商品。然而，零售平台在理解客户行为方面面临挑战，难以预测其未来购买意向。为克服这些难题，本研究提出一种混合零售深度神经网络（Ret-DNN）与极限梯度提升（XGBoost）模型，用于捕捉零售数据中的时序特征和表格动态。首先，数据来源于一家英国在线零售商，包含近50万条交易记录。随后，通过数据清洗、异常值处理、时序特征提取、特征编码及Z分数标准化等一系列技术对采集数据进行预处理，确保数据可用于模型训练与测试。接着，将预处理后的数据输入Ret-DNN模型，该模型作为特征提取器，用于理解客户交易的完整上下文。进一步，将提取的特征输入XGBoost模型，最终输出客户购买概率。最后，所提出的Ret-DNN-XGBoost模型取得了更优结果，平均绝对误差（MAE）达到0.2193，优于现有Ret-DNN模型。关键词：客户行为预测；极限梯度提升；电子商务；预测分析；零售深度神经网络。