A Personalized Product Design and Recommendation System Using LSTM for Enhanced User Shopping Behavior Analysis

This paper aims to deeply analyze user shopping behavior data by introducing the Long Short-Term Memory (LSTM) model in order to solve the shortcomings of traditional methods in data processing ability, model generalization ability, personalized effects and dynamic response ability, and achieve more precise and personalized product design. First, data cleaning and standardization are carried out on user shopping behavior data, followed by feature extraction and time series feature construction of user behavior. Then, a deep neural network structure containing multiple layers of LSTM units can be constructed, and the model’s learning rate, number of hidden layer nodes and other parameters can be adjusted through cross-validation. Finally, the constructed LSTM model can be trained and validated, and the trained LSTM model can be used to predict the future shopping behavior of users. Based on the results, personalized product recommendations and design suggestions can be provided to users. The experimental results show that the values of the evaluation indicators of LSTM model accuracy, recall, F1-score and AUC-ROC value (Area Under Curve-Receiver Operating Characteristic) are 0.92, 0.90, 0.91 and 0.95, respectively. It is significantly better than the traditional Linear Regression, Support Vector Machine (SVM) and Decision Tree methods (DT). This validates the advantages of the LSTM model in processing complex time series data and capturing user behavior patterns. This conclusion demonstrates that the LSTM model can still increase the precision and usefulness of personalized recommendation systems, even though more optimizations are still needed for the number of data samples and model applicability.