Krishna E S Phalguna, Ramu T Bhargava, Chaitanya R Krishna, Ram M Sitha, Balayesu Narasimhula, Gandikota Hari Prasad, Jagadesh B N
Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India.
Department of Electrical and Electronics Engineering, MLR Institute of Technology, Hyderabad, 500043, Telangana, India.
Sci Rep. 2025 Feb 25;15(1):6739. doi: 10.1038/s41598-025-91275-7.
The rapid growth of e-commerce has made product recommendation systems essential for enhancing customer experience and driving business success. This research proposes an advanced recommendation framework that integrates sentiment analysis (SA) and collaborative filtering (CF) to improve recommendation accuracy and user satisfaction. The methodology involves feature-level sentiment analysis with a multi-step pipeline: data preprocessing, feature extraction using a log-term frequency-based modified inverse class frequency (LFMI) algorithm, and sentiment classification using a Multi-Layer Attention-based Encoder-Decoder Temporal Convolution Neural Network (MLA-EDTCNet). To address class imbalance issues, a Modified Conditional Generative Adversarial Network (MCGAN) generates balanced oversamples. Furthermore, the Ocotillo Optimization Algorithm (OcOA) fine-tunes the model parameters to ensure optimal performance by balancing exploration and exploitation during training. The integrated system predicts sentiment polarity-positive, negative, or neutral-and combines these insights with CF to provide personalized product recommendations. Extensive experiments conducted on an Amazon product dataset demonstrate that the proposed approach outperforms state-of-the-art models in accuracy, precision, recall, F1-score, and AUC. By leveraging SA and CF, the framework delivers recommendations tailored to user preferences while enhancing engagement and satisfaction. This research highlights the potential of hybrid deep learning techniques to address critical challenges in recommendation systems, including class imbalance and feature extraction, offering a robust solution for modern e-commerce platforms.
电子商务的快速发展使得产品推荐系统对于提升客户体验和推动商业成功至关重要。本研究提出了一种先进的推荐框架，该框架整合了情感分析（SA）和协同过滤（CF），以提高推荐准确性和用户满意度。该方法涉及基于多步骤流程的特征级情感分析：数据预处理、使用基于对数词频的改进逆类频率（LFMI）算法进行特征提取，以及使用基于多层注意力的编码器 - 解码器时间卷积神经网络（MLA - EDTCNet）进行情感分类。为了解决类别不平衡问题，改进的条件生成对抗网络（MCGAN）生成平衡的过采样。此外，奥科蒂约优化算法（OcOA）对模型参数进行微调，以通过在训练期间平衡探索和利用来确保最佳性能。该集成系统预测情感极性——积极、消极或中性——并将这些见解与CF相结合，以提供个性化的产品推荐。在亚马逊产品数据集上进行的大量实验表明，所提出的方法在准确性、精确率、召回率、F1分数和AUC方面优于现有模型。通过利用SA和CF，该框架在增强用户参与度和满意度的同时，提供符合用户偏好的推荐。本研究突出了混合深度学习技术在解决推荐系统中的关键挑战（包括类别不平衡和特征提取）方面的潜力，为现代电子商务平台提供了一个强大的解决方案。
