计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2010年
4期
1079-1082
,共4页
协同过滤%矩阵稀疏性%奇异值分解%增强的Pearson相关系数
協同過濾%矩陣稀疏性%奇異值分解%增彊的Pearson相關繫數
협동과려%구진희소성%기이치분해%증강적Pearson상관계수
collaboration filtering%matrix sparesity%ingular Value Decomposition (SVD)%Enhanced Pearson Correlation Coefficient (EPCC)
应用奇异值算法得到一个无缺失的矩阵,引进了一种增强的、基于参数的Pearson相关系统算法来提高相关性算法的准确性.提出一个基于奇异值分解和增强Pearson系数的"HybridSVD"算法,用MovieLens数据集来评价该算法,并和其他经典的传统算法做了比较.实验结果证明,"HybridSVD"算法比其他传统算法能更好地处理协同过滤中的稀疏性问题.
應用奇異值算法得到一箇無缺失的矩陣,引進瞭一種增彊的、基于參數的Pearson相關繫統算法來提高相關性算法的準確性.提齣一箇基于奇異值分解和增彊Pearson繫數的"HybridSVD"算法,用MovieLens數據集來評價該算法,併和其他經典的傳統算法做瞭比較.實驗結果證明,"HybridSVD"算法比其他傳統算法能更好地處理協同過濾中的稀疏性問題.
응용기이치산법득도일개무결실적구진,인진료일충증강적、기우삼수적Pearson상관계통산법래제고상관성산법적준학성.제출일개기우기이치분해화증강Pearson계수적"HybridSVD"산법,용MovieLens수거집래평개해산법,병화기타경전적전통산법주료비교.실험결과증명,"HybridSVD"산법비기타전통산법능경호지처리협동과려중적희소성문제.
This paper applied singular value decomposition to predict the missing data. An enhanced Pearson correlation coefficient algorithm based on parameter was introduced to increase the accuracy when computing the similarity of user and items. Finally, a new algorithm called "HybridSVD" was explored, which was based on singular value decomposition and our novel similarity model. In the experiment section, the authors evaluated this new algorithm using the dataset MoiveLens and the results suggest that the new algorithm can better handle this matrix sparsity problem.
