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计算机系统应用:2020,29(6):204-210
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可视化支持下CNN在个性化推荐算法中的应用
(1.忻州师范学院 计算机系, 忻州 034000;2.太原理工大学 计算机科学与技术学院, 太原 030024;3.西安工业大学 西北兵器工业研究院, 西安 710021)
Application of CNN in Personalized Recommendation Algorithms Supported by Visualization
(1.Department of Computer Scienceand Technology, Xinzhou Teachers University, Xinzhou 034000, China;2.College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China;3.Northwest Institutes of Advanced Technology, Xi’an Technological University, Xi’an 710021, China)
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投稿时间:2019-11-08    修订日期:2019-12-09
中文摘要: 传统的基于协同过滤的推荐方法可以挖掘出评分中隐含的特征, 但推荐过程时间长, 且评分矩阵具有稀疏性, 导致样本真实值与预测值间误差较大. 神经网络通过批量训练可以较快计算出对象特征, 卷积神经网络的局部感知与参数共享性使参数个数明显缩减, 利用普通神经网络及卷积神经网络共同实现推荐可使计算时间缩短; 通过调整神经网络的参数, 为卷积神经网络设计合理的特征向量和卷积核大小, 可以提升推荐速度和推荐准确性. 实验表明, 使用神经网络结合卷积神经网络进行推荐的方法能使推荐的绝对误差均值下降至0.67以下, 大幅提升推荐的准确性及有效性.
Abstract:The traditional recommendation method based on collaborative filtering can mine the hidden features in the score, but the recommendation process takes a long time, and the score matrix is sparse, resulting in a large error between the real value of the sample and the predicted value. Neural network can quickly calculate the characteristics of objects through batch training. The number of parameters is significantly reduced by the local perception and parameter sharing of convolutional neural network. The calculation time can be shortened by using common neural network and convolutional neural network to realize recommendation together. By adjusting the parameters of neural network, the reasonable feature vector and convolution kernel size for convolutional neural network can be designed to improve recommendation speed and accuracy. Experimental results show that the method of combining neural network with convolutional neural network can reduce the mean value of absolute error of recommendation to below 0.67, and greatly improve the accuracy and effectiveness of recommendation.
文章编号:7451     中图分类号:    文献标志码:
基金项目:国家自然科学基金面上项目(61876124); 忻州师范学院教学改革项目(JG201813); 忻州师范学院院级科研项目(2019ky02)
引用文本:
宗春梅,张月琴,赵青杉,郝耀军,郭玥鑫.可视化支持下CNN在个性化推荐算法中的应用.计算机系统应用,2020,29(6):204-210
ZONG Chun-Mei,ZHANG Yue-Qin,ZHAO Qing-Shan,HAO Yao-Jun,GUO Yue-Xin.Application of CNN in Personalized Recommendation Algorithms Supported by Visualization.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):204-210

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