🤖 AI Summary
This study addresses the challenge of accurately predicting users’ future purchasing behavior on e-commerce platforms. To this end, the authors propose a novel hybrid model that integrates a Recurrent Temporal Deep Neural Network (Ret-DNN) with XGBoost. Specifically, Ret-DNN is employed for the first time as a sequential feature extractor to learn deep representations from users’ transaction sequences, and its outputs are subsequently fed into XGBoost to predict purchase probabilities. This architecture effectively combines the strengths of deep learning in capturing temporal dependencies with the predictive power of gradient-boosted decision trees. Evaluated on a real-world UK e-commerce dataset comprising 500,000 records, the proposed model achieves a mean absolute error (MAE) of 0.2193, significantly outperforming the standalone Ret-DNN and demonstrating both the effectiveness and innovation of the approach in customer behavior prediction.
📝 Abstract
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.