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    • 1. 发明申请
    • EVALUATION PREDICTING DEVICE, EVALUATION PREDICTING METHOD, AND PROGRAM
    • 评估预测设备,评估预测方法和程序
    • US20110302126A1
    • 2011-12-08
    • US13112684
    • 2011-05-20
    • Masashi SEKINO
    • Masashi SEKINO
    • G06N5/02
    • G06F17/3053G06F17/30867G06N99/005
    • Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.
    • 本发明公开了一种评估预测装置,包括:估计部,被配置为定义多个第一潜在矢量,多个第二潜向矢量,评估值,多个第一特征向量,多个第二特征向量,第一投影矩阵 和第二投影矩阵,表示第一潜在向量和第二潜在向量,并且使用第一特征向量,第二特征向量和已知的评估值作为学习数据来执行贝叶斯估计,并且计算一个 参数组包括第一潜向矢量,第二潜向矢量,第一投影矩阵和第二投影矩阵; 以及预测部,被配置为基于参数组的后验分布来计算未知的评估值的分布。
    • 2. 发明授权
    • Evaluation predicting device, evaluation predicting method, and program
    • 评估预测装置,评价预测方法和程序
    • US08805757B2
    • 2014-08-12
    • US13112684
    • 2011-05-20
    • Masashi Sekino
    • Masashi Sekino
    • G06F15/18G06F19/24
    • G06F17/3053G06F17/30867G06N99/005
    • Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.
    • 本发明公开了一种评估预测装置,包括:估计部,被配置为定义多个第一潜在矢量,多个第二潜向矢量,评估值,多个第一特征向量,多个第二特征向量,第一投影矩阵 和第二投影矩阵,表示第一潜在向量和第二潜在向量,并且使用第一特征向量,第二特征向量和已知的评估值作为学习数据来执行贝叶斯估计,并且计算一个 参数组包括第一潜向矢量,第二潜向矢量,第一投影矩阵和第二投影矩阵; 以及预测部,被配置为基于参数组的后验分布来计算未知的评估值的分布。
    • 4. 发明申请
    • RATING PREDICTION DEVICE, RATING PREDICTION METHOD, AND PROGRAM
    • 评估预测设备,评估预测方法和程序
    • US20120059788A1
    • 2012-03-08
    • US13222638
    • 2011-08-31
    • Masashi SEKINO
    • Masashi SEKINO
    • G06N5/02
    • G06Q30/0282G06Q10/101
    • Provided is a rating prediction device including a posterior distribution calculation unit for taking, as a random variable according to a normal distribution, each of a first latent vector indicating a latent feature of a first item, a second latent vector indicating a latent feature of a second item, and a residual matrix Rh of a rank h (h=0 to H) of a rating value matrix whose number of ranks is H and which has a rating value expressed by an inner product of the first and second latent vectors as an element and performing variational Bayesian estimation that uses a known rating value given as learning data, and thereby calculating variational posterior distributions of the first and second latent vectors, and a rating value prediction unit for predicting the rating value that is unknown by using the variational posterior distributions of the first and second latent vectors.
    • 提供了一种评价预测装置,其包括:后验分布计算单元,用于根据正态分布获取指示第一项目的潜在特征的第一潜在矢量,表示第一项目的潜在特征的第二潜在矢量; 第二项,以及等级数为H的评级值矩阵的秩h(h = 0〜H)的残差矩阵Rh,其具有由第一和第二潜在向量的内积表示的评级值作为 并且使用给定为学习数据的已知评级值进行变分贝叶斯估计,从而计算第一和第二潜在向量的变分后验分布,以及评估值预测单元,用于通过使用变分后验来预测未知的评分值 第一和第二潜在载体的分布。