WEB PAGE INTERACTION PREDICTION WITH LSTM

This paper introduces a novel approach to address the challenge of predicting web page interactions utilizing Long Short-Term Memory (LSTM) networks. The problem at hand involves anticipating user interactions with web pages to enhance user experience and tailor content delivery. Our methodology employs the LSTM model architecture, leveraging its ability to capture sequential dependencies in data. The research method involves training the LSTM model on a carefully selected dataset, employing specific pre-processing steps and data augmentation techniques. The evaluation of our approach includes assessing its performance on a designated dataset, utilizing relevant evaluation metrics. Comparative analysis with existing methods in the literature underscores the effectiveness of our proposed model. Our results demonstrate the superior performance of the LSTM-based approach, showcasing its potential applications in optimizing web page interaction prediction. The paper contributes to the field of web page interaction prediction within the realm of machine learning by introducing an innovative model architecture and providing empirical evidence of its efficacy.