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1.发明授权公开(公告)号：US08880439B2
公开(公告)日：2014-11-04
申请号：US13405796
申请日：2012-02-27
CPC分类号： G06N99/005 , G06K9/6256 , G06N3/08 , G06N7/005
摘要： In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column.
摘要翻译： 在推荐方法中，对具有用户和项目维度的矩阵执行贝叶斯矩阵因子分解（BMF），并且对于由用户做出的项目包含用户评级的矩阵元素来执行，以便训练概率协同过滤模型。 使用概率协同过滤模型为用户生成一个建议。 该推荐可以包括预测项目评级，或者一个或多个推荐项目的标识。 推荐方法由电子数据处理装置适当地执行。 BMF可以使用非高斯先验，例如Student-t先验。 BMF可以附加地或替代地使用包含先验的异方差噪声模型，其包括（1）取决于矩阵行的行依赖方差分量，以及（2）依赖于矩阵列的列依赖方差分量。
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2.发明申请MULTI-TASK LEARNING USING BAYESIAN MODEL WITH ENFORCED SPARSITY AND LEVERAGING OF TASK CORRELATIONS 有权使用贝叶斯模型进行多任务学习，具有强大的空间和任务对应关系公开(公告)号：US20130151441A1
公开(公告)日：2013-06-13
申请号：US13324060
申请日：2011-12-13
IPC分类号： G06F15/18
摘要： Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.
摘要翻译： 多任务回归或分类包括优化表示D特征和P任务之间的关系的贝叶斯模型的参数，其中D> = 1和P> = 1，分别对应于包括用于 P任务。 贝叶斯模型包括先前具有维度D和P的特征和任务维度的矩阵变量。 矩阵变量先验被划分成多个块，并且贝叶斯模型的参数的优化包括推导出先前分布的矩阵变量的块，从而引起多个块的稀疏。 使用优化的贝叶斯模型，为D特征的一组输入值预测P任务的值。 优化还包括将矩阵变量分解成矩阵乘积，包括在任务维度中编码任务之间的相关性的减少秩的矩阵。
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3.发明授权Multi-task learning using bayesian model with enforced sparsity and leveraging of task correlations 有权使用具有强制稀疏性和利用任务相关性的贝叶斯模型进行多任务学习公开(公告)号：US08924315B2
公开(公告)日：2014-12-30
申请号：US13324060
申请日：2011-12-13
摘要： Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.
摘要翻译： 多任务回归或分类包括优化表示D特征和P任务之间的关系的贝叶斯模型的参数，其中D≥1和P≥1，分别对应于包含P任务的值所注明的D特征值的值的训练数据 。 贝叶斯模型包括先前具有维度D和P的特征和任务维度的矩阵变量。 矩阵变量先验被划分成多个块，并且贝叶斯模型的参数的优化包括推导出先前分布的矩阵变量的块，从而引起多个块的稀疏。 使用优化的贝叶斯模型，为D特征的一组输入值预测P任务的值。 优化还包括将矩阵变量分解成矩阵乘积，包括在任务维度中编码任务之间的相关性的减少秩的矩阵。
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4.发明申请公开(公告)号：US20130226839A1
公开(公告)日：2013-08-29
申请号：US13405796
申请日：2012-02-27
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06K9/6256 , G06N3/08 , G06N7/005
摘要： In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column.
摘要翻译： 在推荐方法中，对具有用户和项目维度的矩阵执行贝叶斯矩阵因子分解（BMF），并且对于由用户做出的项目包含用户评级的矩阵元素来执行，以便训练概率协同过滤模型。 使用概率协同过滤模型为用户生成一个建议。 该推荐可以包括预测项目评级，或者一个或多个推荐项目的标识。 推荐方法由电子数据处理装置适当地执行。 BMF可以使用非高斯先验，例如Student-t先验。 BMF可以附加地或替代地使用包含先验的异方差噪声模型，其包括（1）取决于矩阵行的行依赖方差分量，以及（2）依赖于矩阵列的列依赖方差分量。
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