Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization
    1.
    发明授权
    Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization 有权
    通过矩阵分解学习潜在因子模型的有效和容错分布式算法

    公开(公告)号:US09535938B2

    公开(公告)日:2017-01-03

    申请号:US14123259

    申请日:2013-03-15

    申请人: Yahoo! Inc.

    摘要: A method for estimating model parameters. The method comprises receiving a data set related to a plurality of users and associated content, partitioning the data set into a plurality of sub data sets in accordance with the users so that data associated with each user are not partitioned into more than one sub data set, storing each of the sub data sets in a separate one of a plurality of user data storages, each of said data storages being coupled with a separate one of a plurality of estimators, storing content associated with the plurality of users in a content storage, where the content storage is coupled to the plurality of estimators so that the content in the content storage is shared by the estimators, and estimating, asynchronously by each estimator, one or more parameters associated with a model based on data from one of the sub data sets.

    摘要翻译: 一种估计模型参数的方法。 该方法包括:接收与多个用户相关的数据集和相关联的内容,根据用户将数据集划分成多个子数据集,使得与每个用户相关联的数据不被划分成多个子数据集 将每个子数据集存储在多个用户数据存储器中的单独一个中,每个所述数据存储器与多个估计器中的单独一个估计器耦合,将与多个用户相关联的内容存储在内容存储器中, 其中所述内容存储器耦合到所述多个估计器,使得所述内容存储器中的内容由所述估计器共享,并且由每个估计器异步地估计与来自所述子数据之一的数据的与模型相关联的一个或多个参数 套。