Abstract
This study investigates deep learning models for predicting electronic product prices through text sequence (Text2Price). The study examined the performance of these models in terms of price prediction, the factors influencing predictions, the model's comprehension of numerical and expressive text, and the efficacy of the developed price prediction model. The primary aim is to create a model skilled at forecasting product prices using textual sequences containing product names, brands, and features. Methodologically, the research employs the T5-BESD model, a transformer-based architecture trained on a dataset of 22,000 electronic products from the Amazon. Data preprocessing involves cleaning and integrating features to create text sequence. In the model definition phase, a custom neural network architecture, T5Regressor, predicts product prices from textual descriptions. This architecture comprises a transformer-based language model (T5) and a linear regression layer. The T5 model comprehends and encodes the input text, while the linear regression layer predicts the numerical output (price). The linear regression layer involves a transformation with a weight matrix W and bias vector b. Additionally, the L1 loss, used for training, measures the absolute difference between the predicted and true values. In the initialization and setup phase, critical components, including the optimizer (Adam_W), learning rate scheduler, and loss function, are initialized. The learning rate scheduler dynamically adjusts the learning rate during training, incorporating a warm-up phase. The results demonstrate a consistent improvement in accuracy from 38.48–54.86% over five epochs, with the test accuracy reaching 52.38%. The mean squared error decreases from 45057.29 to 19783.88, indicating enhanced prediction accuracy, and the mean absolute error drops from 66.87 to 47.34, reflecting reduced disparities between the predicted and actual values. The research concludes by providing insights into the effectiveness of the T5-BESD model's effectiveness, emphasizing the importance of comprehensive data and suggesting avenues for improvement.