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Hybrid deep learning model for E-commerce recommendations: sentiment analysis with autoencoders and generative adversarial networks

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dc.contributor.authorYarjanli, M.
dc.contributor.authorMahdinasab, N.
dc.contributor.editorMurray, R.
dc.date.issued2025
dc.description.abstractIn 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.
dc.identifier.citationInternational Journal of Intelligent Systems, 2025; 2025(1, article no. 3852068):1-36
dc.identifier.doi10.1155/int/3852068
dc.identifier.issn0884-8173
dc.identifier.issn1098-111X
dc.identifier.urihttps://hdl.handle.net/11541.2/44778
dc.language.isoen
dc.publisherJohn Wiley & Sons
dc.rightsCopyright 2025 Mohammad Yarjanli and Neda Mahdinasab. (http://creativecommons.org/licenses/by/4.0/) Access Condition Notes: Tis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1155/int/3852068
dc.subjectautoencoder
dc.subjecte-commerce
dc.subjectgenerative adversarial network
dc.subjectrecommendation system
dc.subjectreinforcement learning
dc.subjecttransductive learning
dc.titleHybrid deep learning model for E-commerce recommendations: sentiment analysis with autoencoders and generative adversarial networks
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12308325820001831 13308325810001831 9917080050101831
ror.mmsid9917080050101831

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