Vatambeti Ramesh, Gandikota Hari Prasad, Siri D, Satyanarayana G, Balayesu Narasimhula, Karthik M Ganesh, Ch Koteswararao
School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India.
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India.
Sci Rep. 2025 May 18;15(1):17229. doi: 10.1038/s41598-025-02593-9.
Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation. Additionally, Quokka Swarm Optimization (QSO) and Hybrid Mutation-based White Shark Optimizer (HMWSO) are employed for optimizing sampling rates and hyperparameters, respectively. Experiments conducted on the goodbooks-10k and Amazon review datasets demonstrate significant improvements in RMSE, MAE, and R² metrics, proving the superiority of the proposed model over existing deep learning and collaborative filtering techniques. The framework is scalable, interpretable, and applicable across diverse domains, particularly in e-commerce and electronic publishing.
个性化推荐系统对于提高用户满意度和减少信息过载至关重要，尤其是在电子商务平台等数据稀疏的环境中。本文介绍了一种新颖的混合框架，该框架将长短期记忆（LSTM）与改进的分裂卷积（SC）神经网络（LSTM-SC）以及先进的采样技术——自检查自适应合成少数过采样技术（SASMOTE）相结合。与传统的合成少数过采样技术不同，SASMOTE能自适应地选择“可见”最近邻，并采用自检查策略来过滤掉不确定的合成样本，确保生成高质量的数据。此外，分别采用了Quokka群优化（QSO）和基于混合变异的白鲨优化器（HMWSO）来优化采样率和超参数。在goodbooks-10k和亚马逊评论数据集上进行的实验表明，均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R²）指标有显著改善，证明了所提出的模型优于现有的深度学习和协同过滤技术。该框架具有可扩展性，可以解释，并且适用于不同领域，特别是在电子商务和电子出版领域。
