The analysis of dynamic evaluation of online shopping satisfaction based on the recurrent neural network model.

This work aims to accurately understand user satisfaction in online shopping, reflecting user preferences and promoting the development of online shopping. This work explores a behavioral prediction method for online shopping users using a Recurrent Neural Network (RNN) model. Traditional RNN faces challenges in training on long sequences and is susceptible to the vanishing gradient problem. To address this problem, the proposed Gated Recurrent Unit (GRU) introduces a gating mechanism to capture long-term dependencies in sequential data. Building on this, a Dynamic Weighted-GRU (DW-GRU) model is proposed, incorporating a dynamic weighting mechanism based on GRU to adapt to the dynamic changes in online shopping satisfaction. This improvement allows the model to effectively learn and remember long-term dependencies in sequential data while alleviating the vanishing gradient problem. Experimental evaluations of the prediction model are conducted on an Amazon shopping dataset. Comparative analysis reveals that the DW-GRU model outperforms the standard RNN model, showing lower errors with accuracy, precision, and recall values of 0.871, 0.667, and 0.667, respectively. The research findings provide valuable guidance for operators and data analysts of online shopping platforms, furnishing feasible technical support for enhancing user satisfaction and delivering precise product recommendations.