传统的概率矩阵分解在推荐算法中取得了一定的效果,但是仍然面临数据稀疏性问题,并且对数据的利用效率不高,不能根据已有数据准确计算用户(物品)之间的关系,评分预测准确性仍然有待提高.本文利用用户对物品的评分序列信息充分挖掘用户(...传统的概率矩阵分解在推荐算法中取得了一定的效果,但是仍然面临数据稀疏性问题,并且对数据的利用效率不高,不能根据已有数据准确计算用户(物品)之间的关系,评分预测准确性仍然有待提高.本文利用用户对物品的评分序列信息充分挖掘用户(物品)之间的相似度关系,提出了基于用户行为序列的概率矩阵分解推荐算法UBS-PMF(Probability Matrix Factorization Recommendation Algorithm Based on User Behavior Sequence).首先根据用户对物品的评分序列和物品标签信息计算用户对标签的评分序列,即为用户的偏好转移序列,根据该序列可以计算出用户之间的相似度矩阵,同时,用户对物品的评分序列也隐藏着物品之间的关系,利用多个用户对物品的评分序列可以得到物品相似度矩阵,将所得用户(物品)相似度矩阵融入概率矩阵分解模型中进行评分预测,Movielens数据集中的实验表明该算法具有显著的效果,在评分预测准确性方面优于传统的推荐算法.展开更多
The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An incr...The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.展开更多
文摘传统的概率矩阵分解在推荐算法中取得了一定的效果,但是仍然面临数据稀疏性问题,并且对数据的利用效率不高,不能根据已有数据准确计算用户(物品)之间的关系,评分预测准确性仍然有待提高.本文利用用户对物品的评分序列信息充分挖掘用户(物品)之间的相似度关系,提出了基于用户行为序列的概率矩阵分解推荐算法UBS-PMF(Probability Matrix Factorization Recommendation Algorithm Based on User Behavior Sequence).首先根据用户对物品的评分序列和物品标签信息计算用户对标签的评分序列,即为用户的偏好转移序列,根据该序列可以计算出用户之间的相似度矩阵,同时,用户对物品的评分序列也隐藏着物品之间的关系,利用多个用户对物品的评分序列可以得到物品相似度矩阵,将所得用户(物品)相似度矩阵融入概率矩阵分解模型中进行评分预测,Movielens数据集中的实验表明该算法具有显著的效果,在评分预测准确性方面优于传统的推荐算法.
文摘The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.
