Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization
US9535938B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2013 |
| Grant date | Jan 3, 2017 |
| Priority date | — |
| Expiry date | Nov 21, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG09B19/00
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.