Invention Grant
- Patent Title: Parallel collective matrix factorization framework for big data
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Application No.: US14325429Application Date: 2014-07-08
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Publication No.: US10235403B2Publication Date: 2019-03-19
- Inventor: Ryan A. Rossi , Rong Zhou
- Applicant: Palo Alto Research Center Incorporated
- Applicant Address: US CA Palo Alto
- Assignee: Palo Alto Research Center Incorporated
- Current Assignee: Palo Alto Research Center Incorporated
- Current Assignee Address: US CA Palo Alto
- Agency: Fay Sharpe LLP
- Main IPC: G06F17/30
- IPC: G06F17/30 ; G06Q30/02

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.
Public/Granted literature
- US20160012088A1 PARALLEL COLLECTIVE MATRIX FACTORIZATION FRAMEWORK FOR BIG DATA Public/Granted day:2016-01-14
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