Fusion Deep Learning Based on Back Propagation Neural Network for Personalization

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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.