Fusion Deep Learning Based on Back Propagation Neural Network for Personalization
Fusion Deep Learning Based on Back Propagation Neural Network for Personalization
复制标题
基于反向传播神经网络的融合深度学习实现个性化
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sumit Jain
中科院分区：
文献类型：
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作者：
Anand Singh Rajawat;Sumit Jain
互联网用户在信息量、信息访问、信息分类以及处理信息的互联网拓扑结构的复杂性方面都有了巨大的增长。多维数据(移动应用使用数据(混合数据集(日志数据)包含大量不相关的冗余信息。Web应用程序的用户发现，由于数据量巨大(每秒生成的海量数据)，很难快速有效地获取所需的信息。许多机器学习算法对大量的数据集是有用的，以发现分类器的效率和准确性。解决这一问题的有效方案之一是网络个性化。为用户提供个性化的推荐以提高可信度取决于Web应用对Web应用中有用信息的使用情况。预测这样的个性化系统的行为是非常困难的。我们提出的智能地图缩减器模型是基于机器学习的概念。本文的研究结果对基于BP神经网络算法建立的地图归约融合深度学习的大数据集分类问题的训练和测试具有重要意义。该算法使用Hadoop框架实现，通过提高准确率和降低错误率，增强了现有MAP归约模型的性能。
Internet user has caused a tremendous growth in the information quantity, information accesses, information classification and complexity of Internet topologies handling information. Multidimensional data (mobile apps usage data (mix data set (log data))) contains a huge number of irrelevant redundant information. Users of a web application find it difficult to get the required information quickly and effectively due to enormous size of data (huge data generated per second). Numerous machine learning algorithms are useful to an amount of dataset to find the efficiency and accuracy of the classifiers. One of the potent solutions for this problem is web personalization. Providing personalized recommendations to users for improving credibility depend on the web application usage of the useful information in web application. It is very difficult to predict the behavior of such personalization systems. Our proposed intelligent map reducer model is based on machine learning concept. The results of this research are significant for training and testing of big datasets for Map Reduce Fusion Deep Learning Based on Back Propagation Neural Network algorithm established classification problems. The proposed algorithm is implemented using the HADOOP framework and enhanced the performance of existing map reducer model by improving the accuracy and reduce error rate.
DOI：
10.1109/tnnls.2018.2886017
发表时间：
2019-09-01
影响因子：
10.4
作者：
Yu, Xiaoyong;He, Pan;Li, Xiaolin
通讯作者：
Li, Xiaolin