Deep Learning for Dynamic Content Adaptation: Enhancing User Engagement in E-commerce

In recent years, the landscape of online businesses has undergone a significant transformation. With the proliferation of e-commerce websites, providing a personalized user experience has become critical for success. This paper presents a study on deep learning for dynamic content adaptation in e-commerce, aimed at enhancing user engagement. The methodology involved collecting and preprocessing data from an e-commerce website, which included visitor behavior data. A recurrent neural network (RNN) with long short-term memory (LSTM) cells were chosen as the deep learning architecture. The model was trained and evaluated using various performance metrics, such as accuracy, precision, recall, F1-score, click-through rate (CTR), average session duration, and conversion rate. The results demonstrated that the deep learning model outperformed the baseline model in all evaluation metrics. The deep learning model achieved an accuracy of 92%, indicating its success in adapting content based on user behavior.