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Updated: Jul 20, 2025

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深度反复的高斯尼斯特罗夫建议在社交网络中使用多代理
Vinita Tapaskar1, Mallikarjun M Math2
1Visvesvaraya Technological University, Research Center, Jnana Sangama, Machhe, Belagavi, 590018 Karnataka India.
概括
这项研究介绍了深度循环高斯纳斯特罗夫.
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:
- 社交网络中大数据的扩散阻碍了用户提取有价值信息的能力.
- 现有的推系统难以应对现实数据的复杂性和规模.
- 多代理深度学习显示出希望,但大数据中的精确建议仍然是一个挑战.
研究的目的:
- 提出一个新的推系统,深度循环高斯尼斯特罗夫的最佳梯度 (DR-GNOG),以提供最佳和精确的推.
- 通过智能数据处理,应对社交网络信息过载的挑战.
- 提高推系统的准确性,速度和回忆率.
主要方法:
- 该DR-GNOG系统采用多层架构,将深度学习与多代理方法相结合.
- 一个推特累积器代理将用户的推特输入到输入层.
- 第一个隐藏层使用Gaussian Nesterov的最佳梯度来优化推特分类.
- 第二个隐藏层具有深度循环预测推模型,以减轻消失梯度问题.
- 在输出层中使用过度激活函数进行预测性推.
主要成果:
- 根据DR-GNOG方法,相对于现有的GANCF和引导方法,它显著改进.
- 建议准确度提高了13%至21%.
- 推时间提高了22-32%.
- 召回率增加了15%至22%.
结论:
- 拟议的DR-GNOG系统为大数据环境中提供精确和高效的建议提供了卓越的解决方案.
- 多代理深度学习和先进的梯度优化技术的整合有效地解决了社交网络数据的关键挑战.
- 实验结果证实了DR-GNOG在准确性,速度和回忆方面的显著性能提升.
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