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Nonlinear Maps Satisfying Derivability of a Class of Matrix Ring over Commutative Rings
  • ISSN号：2095-2651
  • 期刊名称：Journal of Mathematical Research with Applications
  • 时间：2015.11.1
  • 页码：625-633
  • 分类：TP311[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]江西理工大学应用科学学院,江西赣州341000, [2]江西理工大学理学院,江西赣州341000
  • 相关基金：国家自然科学基金资助项目（11426121）; 江西省教育厅科技基金资助项目（GJJ14434）
  • 相关项目：0-1矩阵若干秩问题的研究
作者： 偶世坤|钟金|
关键词： 推荐系统, 深度学习, 自编码神经网络, ZCA白化, 降噪自编码, recommended system, deep learning, autoencoders neural network, ZCA whitening, denoising autoencoders
中文摘要：

由于自编码神经网络能够提取数据从低层到高层的特征,发现样本间潜在的相关性,为了提高推荐系统的精确度提出一种基于降噪自编码的推荐算法。首先利用ZCA白化对评分数据进行预处理,对处理后的数据加入随机噪声并构建自编码神经网络模型,再对模型进行预训练和微调得出网络权重,最后根据训练的网络权重对测试样本进行重构,预测用户评分并计算评分误差。实验结果表明,基于降噪自编码神经网络能有效提高推荐精度。

英文摘要：

As the autoencoders neural network can extract the features of data from low-level to high-level, and find the potential correlation between the samples, in order to improve the accuracy of the recommendation system, this paper proposes a recommendation algorithm based on denoising autoencoders. Firstly, this paper pretreats score data by ZCA whitening, adds the random noise in the processed data, and builds the autoencoders neural network, then obtains the network weights through the model pretraining, fine-tuning. Finally according to the trained network weights the test sample score is reconstructed to forcast user rating and calculate score error. Experimental results show that denoising autoencoders can effectively improve the recommendation accuracy.

同期刊论文项目
0-1矩阵若干秩问题的研究
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典型群上自同构问题的推广
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期刊信息
  • 《数学研究及应用：英文版》
  • 中国科技核心期刊
  • 主管单位:国家教育部
  • 主办单位:大连理工大学
  • 主编：王仁宏
  • 地址：大连理工大学应用数学系
  • 邮编：116024
  • 邮箱：
  • 电话：0411-84707392
  • 国际标准刊号：ISSN：2095-2651
  • 国内统一刊号：ISSN：21-1579/O1
  • 邮发代号:8-92
  • 获奖情况:
  • 1998年大连市优秀期刊奖,2000年大连市优秀期刊奖
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国数学评论（网络版）,德国数学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊
  • 被引量:36