This research introduces a novel technique for predicting web user access
paths based on Recognition with Recurrent Neural Network (RNN). The study focuses
on utilizing user access paths as the primary research goal and explores the application
of RNN in addressing the path forecasting problem. A network model is developed and
examined for predicting access paths by enhancing the feature layer. This approach
effectively leverages contextual information from user conversation sequences, learns
and memorizes user access patterns, and obtains optimal model parameters through
training data analysis. Consequently, it enables accurate prediction of the user's next
access path. Theoretical analysis and experimental results demonstrate the higher
efficiency and improved accuracy of path forecasting achieved by this technique,
making it well-suited for solving web user access path prediction problems.
Keywords: Path forecasting, Recognition with recurrent neural network, Contextual information, Long short-term memory (LSTM), User access patterns, Web user path prediction.
