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通过自我检查的自适应SMOTE和混合神经网络来增强稀缺数据建议

Ramesh Vatambeti¹, Hari Prasad Gandikota², D Siri³

¹School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India.

Scientific reports

|May 18, 2025

概括

本研究引入了一种新的混合框架,使用长短期内存 (LSTM) 和分割卷积 (SC) 网络,并使用先进的数据采样来提供更好的电子商务建议. 该模型显著提高了准确性,超过了现有的技术.

科学领域:

人工智能的人工智能
机器学习机器学习
数据科学数据科学数据科学

背景情况:

个性化推系统对于用户满意度和管理电子商务中的信息过载至关重要.
数据稀疏性对传统的推算法构成了重大挑战.

研究的目的:

引入一种新的混合框架,LSTM-SC与自检自适应SMOTE (SASMOTE),用于增强个性化的建议.
改善数据质量和模型性能在数据稀疏环境中.

主要方法:

混合框架结合了长短期记忆 (LSTM) 和修改的分割卷积 (SC) 神经网络 (LSTM-SC).
先进的数据采样技术:自检自适应SMOTE (SASMOTE) 用于自适应邻居选择和不确定样本过.
优化算法:用于采样率的Quokka Swarm优化 (QSO) 和用于超参数调整的基于混合突变的白优化器 (HMWSO).

主要成果:

在根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R2指标方面显著改善.
在基准数据集上表现优于现有的深度学习和协作过技术 (goodbooks-10k,亚马逊评论).

结论:

与SASMOTE一起提出的LSTM-SC框架为个性化建议提供了一种优越的方法,特别是在数据稀疏的电子商务环境中.
该框架可扩展,可解释,适用于电子商务和电子出版等多个领域.

关键词:

电子出版公司电子出版公司基于混合突变的白优化器.长期短期记忆长期短期记忆个性化推系统是个性化的推系统.夸卡群群群群群的优化用户信息用户信息

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