通过使用MLA-EDTCNet和协作过来进行情绪分析来增强电子商务建议
E S Phalguna Krishna1, T Bhargava Ramu2, R Krishna Chaitanya3
1Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India.
Scientific reports
|February 25, 2025
概括
本研究介绍了一种先进的电子商务推系统,该系统结合了情绪分析 (SA) 和协作过 (CF). 混合深度学习模型通过分析产品评论来提高推准确性和用户满意度.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 电子商务 技术 技术 电子商务
背景情况:
- 电子商务的增长需要改进产品推系统.
- 当前的系统经常在准确性和用户满意度方面扎.
- 将用户情绪与传统推方法相结合是关键.
研究的目的:
- 开发一个先进的推框架,整合情绪分析 (SA) 和协作过 (CF).
- 为了提高推的准确性,精度,回忆,F1得分和AUC.
- 提高用户对电子商务平台的整体满意度和参与度.
主要方法:
- 使用基于多层注意力的编码解码器临时卷积神经网络 (MLA-EDTCNet) 的特征级情绪分析.
- 用于特征提取的基于日志期频率的修改反向类频率 (LFMI).
- 修改的条件生成对抗网络 (MCGAN) 用于类不平衡和Octillo优化算法 (OcOA) 用于参数调整.
主要成果:
- 拟议的框架在关键性能指标 (准确性,精度,回忆,F1分数,AUC) 上显著优于最先进的模型.
- 集成SA和CF提供了高度个性化的产品建议.
- 在解决阶级不平衡和特征提取挑战方面表现出有效性.
结论:
- 混合深度学习技术,结合SA和CF,为电子商务推系统提供了强大的解决方案.
- 该框架通过准确和个性化的建议来增强用户体验.
- 这种方法在推动在线零售业的业务成功方面具有重大潜力.
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