发明授权
- 专利标题: Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization
- 专利标题(中): 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
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申请号: US14123259申请日: 2013-03-15
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公开(公告)号: US09535938B2公开(公告)日: 2017-01-03
- 发明人: Oren Shlomo Somekh , Edward Bornikov , Nadav Golbandi , Oleg Rokhlenko , Ronny Lempel
- 申请人: Yahoo! Inc.
- 申请人地址: US CA Sunnyvale
- 专利权人: EXCALIBUR IP, LLC
- 当前专利权人: EXCALIBUR IP, LLC
- 当前专利权人地址: US CA Sunnyvale
- 代理机构: Pillsbury Winthrop Shaw Pittman LLP
- 国际申请: PCT/US2013/032385 WO 20130315
- 国际公布: WO2014/143018 WO 20140918
- 主分类号: G06F15/18
- IPC分类号: G06F15/18 ; G06F17/30 ; G06Q30/06 ; G09B19/00
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.
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