Deep Learning in Predicting Consumer Purchase Intentions

This study successfully identified consumer purchase intentions by developing and optimizing a deep learning model. Key applications include personalized recommendation systems in e-commerce and click-through rate prediction in online advertising. Through a comprehensive case analysis, the model's effectiveness and reliability in real-world scenarios are validated through essential processes such as data preprocessing, feature extraction, and model training. The findings demonstrate that the integration of deep learning techniques significantly improves the accuracy of recommendation systems and enhances the prediction performance of advertising click-through rates, ultimately leading to a better user experience and higher business revenue. Performance metrics such as precision, recall, F1 score, and AUC-ROC highlight the model's exceptional capabilities, underscoring the broad applicability and vast potential of deep learning methods across various industries. This study not only offers theoretical insights but also confirms the practical value of deep learning through experimental results, providing a solid foundation for future research and applications.