Development of a Probabilistic Model for Predicting Advertising Conversion Using Recurrent Neural Networks

This article covers a comprehensive study in the field of website traffic prediction where machine learning is used. It also additionally focuses on a specialized neural network structure utilizing LSTM layers and an attention layer, explicitly crafted for the processing of time series data. The paper extensively discusses LSTM layers and their ability to capture long-term dependencies in data, as well as the integration of an attention layer to focus on key moments. The article also includes the stages of model training, from hyperparameter tuning to evaluating results on test data. Hyperparameters such as hidden layer size, initial learning rate, and others were carefully tuned to strike a balance between performance and training resources. Model evaluation was conducted using mean absolute error (MAE) and mean squared error (MSE).