A Hybrid Deep Learning Model for E-Commerce Recommendations: Sentiment Analysis With Autoencoders and Generative Adversarial Networks

Abstract

In e-commerce, customer reviews wield significant influence over business strategies. This study proposes a robust sentiment analysis (SA) model tailored to e-commerce recommendations. It aims to address the key limitations of existing methods, including challenges in generalizability, feature extraction, class imbalance, and hyperparameter tuning. Our process uses an autoencoder (AE) to extract key features from the input sentence. We employ DistilBERT for word embedding, which performs faster than the standard BERT model (bidirectional encoder representations from transformers). The proposed architecture integrates an AE with a transductive long short-term memory (TLSTM) unit, which is trained with a modified generative adversarial network (GAN). TLSTM leverages transductive learning to emphasize training samples that closely resemble those in the test distribution, enhancing the flexibility and predictive accuracy of the model. Within the GAN, the generator is designed to exclude gradients from dominant batch instances, encouraging greater output diversity and generalization. Once the AE is trained, its compressed feature representations are fed into a multilayer perceptron (MLP) classifier. To tackle class imbalance issues during classification, we implement a reinforcement learning (RL) mechanism. This strategy prioritizes the minority class by applying a reward mechanism to balance the classification outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) algorithm to fine-tune the hyperparameters of the model. Experimental results on the AIV, AA, and Yelp datasets demonstrate superior performance, with F-measure scores of 91.603%, 89.504%, and 90.397%, respectively. These outcomes validate the robustness of the model and its potential to significantly enhance recommendation quality in dynamic e-commerce environments.