Parallel collective matrix factorization framework for big data
US10235403B2 · kind B2 · utility
5Cited by
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Key dates
| Filing date | Jul 8, 2014 |
| Grant date | Mar 19, 2019 |
| Priority date | — |
| Expiry date | Jun 28, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and a method perform matrix factorization. According to the system and the method, at least one matrix is received. The at least one matrix is to be factorized into a plurality of lower-dimension matrices defining a latent feature model. After receipt of the at least one matrix, the latent feature model is updated to approximate the at least one matrix. The latent feature model includes a plurality of latent features. Further, the update performed by cycling through the plurality of latent features at least once and alternatingly updating the plurality of lower-dimension matrices during each cycle.
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