CAJ | 학술논문

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。
구진기이치분해기술이경피엄범응용재개성화추천계통지중。통과구진기이치분해가이제고개성화추천적준학도。전통적기이치분해모형대정개구진진행분해,득도 user 화 item 량개특정구진,연후진행평분예측,병미고필불동범위적평분포함적불동신식。통과계산평분중적림계치,파평분구진탁분성량개구진,칭위정반궤구진화부반궤구진。재기우량개반궤구진적특정래완성대평분적예측。재실험수거방면,사용MovieLens적수거집,대전통적기이치분해모형(SVD)화기우초도적기이치분해모형(HSVD)진행개진。실험결과표명,인입편호구분개념적모형PSVD、PHSVD,기추천효과도우우원모형。
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.