Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

作     者：Yang Li Fei Yin Xianghui Hui 

作者机构：College of Information and Management ScienceHenan Agricultural UniversityZhengzhou450046China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第5期

页      面：4047-4063页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：Data extraction recommendation algorithm CNN algorithm attention model 

摘      要：With the rapid development of the Internet globally since the 21st century,the amount of data information has increased *** helps improve people’s livelihood and working conditions,as well as learning ***,data extraction,analysis,and processing have become a hot issue for people from all walks of *** recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low *** solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network *** the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ***,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes *** recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction *** obtains data name features through a convolution ***,the top pooling layer obtains the length *** attention system layer obtains the characteristics of the data *** results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present *** proposed algorithm shows excellent accuracy and robustness.