An Efficient Deep Learning Framework for Real-Time Product Recommendation in E-Commerce

E-commerce platforms generate massive volumes of user-generated product reviews, making sentiment-aware recommendation systems essential for improving personalization and decision-making. This study aims to develop a high-performance real-time product recommendation framework by integrating sentiment analysis with deep learning techniques. The proposed method utilizes Amazon review data, which is preprocessed through feature extraction using TF-IDF, followed by class balancing using SMOTE. Accurate sentiment categorization in user evaluations is achieved by using a stacked LSTM-based DL model that captures contextual and sequential relationships. Accuracy, Precision, Recall, F1-score, and AUC-ROC measures are used for model evaluation after training using Binary Cross-Entropy loss and the Adam optimizer. Based on the experimental findings, the suggested model regularly beats more conventional ML models like Decision Tree, Logistic Regression, and Naïve Bayes, as well as other DL methods like RNN and DeepFM, with an accuracy rate of 98.42%. This study primarily contributes by enhancing the relevance of recommendations in real-time via the merging of balanced learning and sentiment-driven LSTM modeling. In conclusion, the framework provides a scalable, accurate, and robust solution for large-scale deployment in modern e-commerce systems.

E-commerce, User Reviews, Product Recommendation, Sentiment Analysis, Deep Learning, LSTM, Amazon product review data

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