[1]李晓峰,李 东.基于深度学习的高维稀疏数据组合推荐算法[J].计算机技术与发展,2020,30(02):104-108.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 021]
　LI Xiao-feng,LI Dong.A High-dimensional Sparse Data Combination Recommendation Algorithm Based on Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(02):104-108.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 021]
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基于深度学习的高维稀疏数据组合推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年02期
页码:
104-108
栏目:
智能、算法、系统工程
出版日期:
2020-02-10

文章信息/Info

Title:
A High-dimensional Sparse Data Combination Recommendation Algorithm Based on Deep Learning
文章编号:
1673-629X(2020)02-0104-05
作者:
李晓峰1; 李 东2
1. 黑龙江外国语学院 信息工程系,黑龙江 哈尔滨 150025; 2. 哈尔滨工业大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
Author(s):
LI Xiao-feng1; LI Dong2
1.Department of Information Engineering,Heilongjiang International University,Harbin 150025,China; 2.School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China
关键词:
深度学习; 高维稀疏数据; 组合推荐; 特征提取; 挖掘
Keywords:
deep learning; high dimensional sparse data; combination recommendation; feature extraction; mining
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 02. 021
摘要:
传统方法在对高维稀疏数据进行检测的过程中,受到高维特征扰动的影响,数据误差较大,因此提出一种基于深 度学习的高维稀疏数据组合推荐算法。 采用相空间重构方法进行高维稀疏数据的特征重构,根据重构结果结合非线性统 计序列分析方法进行高维稀疏数据的回归分析和点云结构重组,在此基础上提取高维稀疏数据的组合特征量;依据特征 量提取结果采用特征提取技术抽取高维稀疏数据的平均互信息特征量,并结合关联规则挖掘方法进行高维稀疏数据的主 成分分析,挖掘高维稀疏数据的相似度属性类别成分,最终采用深度学习方法进行高维稀疏数据组合推荐过程中的自适 应寻优,实现高维稀疏数据的组合推荐。 仿真结果表明,采用该算法进行高维稀疏数据推荐的属性归类辨识性较好,特征分辨能力较强,提高了数据的检测和识别能力。
Abstract:
In the process of detecting high-dimensional sparse data,the traditional methods are affected by high-dimensional feature perturbation,and the data error is large. Therefore,a high-dimensional sparse data combination recommendation algorithm based on deep learning is proposed. The phase space reconstruction method is used to reconstruct the features of high-dimensional sparse data. Based on the reconstructed results and the non-linear statistical sequence analysis method,the regression analysis of high-dimensional sparse data and the reorganization of point cloud structure are carried out. On this basis,the combined features of high-dimensional sparse data are extracted. According to the results of feature extraction,the average mutual information features of high-dimensional sparse data are extracted by feature extraction technology. Quantity,principal component analysis of high-dimensional sparse data is combined with association rule mining method,and similarity attribute category components of high-dimensional sparse data are mined. Finally,deep learning method is used for self-adaptive optimization in the process of high-dimensional sparse data combination recommendation, which realizes the combination recommendation of high-dimensional sparse data. The simulation shows that the proposed method has better recognition ability for attribute classification and feature resolution of high-dimensional sparse data recommendation,and improves the detection and recognition ability of data.

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更新日期/Last Update: 2020-02-10