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基于无模糊相似图像的自适应VPKNN-NET算法的时尚产品推
1Department of Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, India.
Frontiers in big data
|August 25, 2025
概括
这项研究引入了适应性VPKNN网络算法,可显著提高时尚建议的准确性和效率. 这种新的方法提高了视觉相似性的评估,以获得更好的电子商务体验,特别是在具有挑战性的场景中.
科学领域:
- 计算机科学
- 人工智能
- 电子商务技术
背景情况:
- 推系统对于电子商务至关重要,
- 视觉驱动的行业,如时尚,对传统的基于关键字的推系统构成挑战.
- 传统方法很难有效地捕捉主观风格偏好.
研究的目的:
- 提出一种使用自适应VPKNN算法的新型时尚推框架.
- 提高视觉相似性的评价.
- 解决主观风格偏好检测现有系统的局限性.
主要方法:
- 使用预训练VGG16卷积神经网络 (CNN) 进行深度视觉特征提取.
- 通过主要组件分析 (PCA) 减少尺寸.
- 一个修改的K-近邻 (KNN) 算法,集成欧几里德和共弦相似度指标.
主要成果:
- 该系统的准确性高达98.69%.
- 与基线模型相比,较低的根平均平方误差 (RMSE) 为0. 8213和平均绝对误差 (MAE) 为0. 6045.
- 优于随机森林,支持矢量机 (SVM) 和标准KNN.
结论:
- 适应性VPKNN网络框架显著提高了视觉时尚建议的精度,可解读性和效率.
- 有效地克服模糊相似模型的局限性.
- 为视觉导向的电子商务提供可扩展的解决方案,特别是用于冷启动和低数据条件.
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