摘要
【目的】传统的协同过滤推荐模型无法提取到用户与项目之间复杂的交互关系,这对于最终的推荐结果会造成一定的不良影响。【方法】针对这一问题,本文提出了一种混合推荐模型DAAI(Denoising Autoencoder with Attribute Information),采用降噪自编码器提取评分矩阵中的深层次非线性特征,在此基础上,使用DNN、CNN等方式提取属性信息中隐藏的特征,最后通过多层感知机融合多种特征得到最终的预测评分。【结论】将该模型在电影数据集MovieLens上进行实验,与奇异矩阵分解(SVD)、概率矩阵分解(PMF)、AutoRec等传统推荐算法进行比较,实验结果表明DAAI模型具有更好的推荐效果。【局限】神经网络结构较为复杂,所以本文的模型相较于传统的推荐模型训练时间有所增加。
[Objective]The traditional collaborative filtering recommendation models cannot extract the complex interactive relationship between users and projects,which causes a certain adverse impact on the final recommendation results.[Methods]To solve this problem,a hybrid recommendation model DAAI(Denoising Autoencoder with Attribute Information)is proposed in this paper.The denoising autoencoder is used to extract the deep nonlinear features of the scoring matrix.On this basis,DNN,CNN,and other methods are used to extract the hidden features in the attribute information.Finally,a multi-layer perceptron is adopted to generate the final prediction score by aggregating various features.[Conclusions]The proposed model is tested on the MovieLens dataset and compared with the traditional recommendation algorithms such as SVD,PMF,and AutoRec.The experiment results show that the DAAI model can achieve better recommendation results.[Limitations]Due to the complex neural network structure,the training cost of our model increases slightly compared with traditional models.
作者
陈子健
李俊
岳兆娟
赵泽方
CHEN Zijian;LI Jun;YUE Zhaojuan;ZHAO Zefang(Computer Network Information Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《数据与计算发展前沿》
CSCD
2021年第3期148-155,共8页
Frontiers of Data & Computing
基金
面向科学大数据传输的全球科研创新平台GRP关键技术研究(241711KYSB20180002)
国家自然科学基金面上项目“命名数据网络多源多路径传输控制机制研究”(61672490)。
关键词
自编码器
卷积神经网络
深度学习
推荐模型
autoencoder
convolutional neural network
deep learning
recommendation model
