Hybrid Model Integration of LightGBM, DeepFM, and DIN for Enhanced Purchase Prediction on the Elo Dataset

In the rapidly evolving landscape of personalized recommendation systems, accurately predicting user purchase behavior remains a critical challenge. This study presents a novel hybrid model combining LightGBM, DeepFM, and Deep Interest Network (DIN) architectures to enhance the prediction accuracy for the Elo payment dataset. Our approach leverages advanced feature engineering, including clustering and temporal feature extraction, coupled with robust training strategies such as adversarial weight perturbation (AWP) and pseudo-labeling. By integrating these techniques, we achieve superior performance compared to existing models, as measured by AUC (Area Under the Curve) and NDCG (Normalized Discounted Cumulative Gain) metrics. Our model demonstrates a significant improvement in prediction precision, particularly for high-ranking predictions, thereby offering a comprehensive solution to the complexities of user behavior modeling in payment datasets.