基于正负反馈矩阵的SVD推荐模型

摘要

矩阵奇异值分解技术已经被广泛应用在个性化推荐系统之中。通过矩阵奇异值分解可以提高个性化推荐的准确度。传统的奇异值分解模型对整个矩阵进行分解,得到 user 和 item 两个特征矩阵,然后进行评分预测,并未考虑不同范围的评分包含的不同信息。通过计算评分中的临界值,把评分矩阵拆分成两个矩阵,称为正反馈矩阵和负反馈矩阵。再基于两个反馈矩阵的特征来完成对评分的预测。在实验数据方面,使用MovieLens的数据集,对传统的奇异值分解模型(SVD)和基于超图的奇异值分解模型(HSVD)进行改进。实验结果表明,引入偏好区分概念的模型PSVD、PHSVD,其推荐效果都优于原模型。%Singular value decomposition technique has been widely used among the personalized recommendation system. By matrix singular value decomposition can improve the accuracy of personalized recommendations. The traditional model only do the singular value decomposition of the matrix is decomposed into user feature matrix and item feature matrix, and the prediction score is not considered different information containing a different range of scores. By calculating scores critical value, the scoring matrix split into two matrices, called positive feedback matrix and negative feedback matrix. Then two feedback matrices based on the feature to complete the scoring in the prediction. In the experimental data, as used herein MovieLens data sets, the traditional model of singular value decomposition (SVD)and based on hypergraph singular value decomposition model (HSVD)to improve it. Experimental results show that the effects of PSVD, PHSVD model are better than the original model.