HRS-DC:基于深度学习的混合推荐模型

HRS-DC:Hybrid Recommendation Model Based on Deep Learning

针对传统的矩阵分解算法,仅利用评分信息作为推荐依据,当评分数据稀疏时,不能准确获取隐式反馈,影响推荐的准确性,充分利用辅助信息进行隐式特征的提取成为研究热点之一,提出一种基于深度学习的推荐模型HRS-DC,利用深度神经网络和卷积神经网络从辅助信息中分别提取出用户和项目的隐性特征向量,再将特征向量经过改进的神经协同过滤得出新的评分矩阵。通过在三个真实的数据集上进行验证,与概率矩阵分解(PMF)、协同过滤主题回归(CTR)、协同过滤深度学习(CDL)、卷积矩阵分解ConvMF算法相比提高了评分预测的准确性,也在一定程度上缓解了冷启动问题。

For the traditional matrix factorization algorithm,only the scoring information is used as the recommendation basis,when the scoring data is sparse,the implicit factor vector cannot be accurately extracted,making full use of auxiliary information has become one of the research hotspot,and a recommendation based on deep learning is proposed,the model HRS-DC uses the deep neural network and the convolutional neural network to extract the recessive feature vectors of the user and the project from the auxiliary information,then transforms the feature vector through the improved neural collaborative filtering to obtain a new scoring matrix.Through verification on three real data sets,the accuracy of scoring prediction is improved compared with Probability Matrix Factorization(PMF),Collaborative filtering Topic Regression(CTR),Collaborative filtering Deep Learning(CDL)and Convolution Matrix Factorization(ConvMF),and the cold start problem is alleviated to some extent.