Automated Compilation of Probabilistic Task Description into Executable Neural Network Specification
US-2018082172-A1 · Mar 22, 2018 · US
System and method for multi-modal graph-based personalization
US11301774B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11301774-B2 |
| Application number | US-201715593353-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 12, 2017 |
| Priority date | Feb 28, 2017 |
| Publication date | Apr 12, 2022 |
| Grant date | Apr 12, 2022 |
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Abstract
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A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned by combining label latent representations from a vertex and neighboring nodes so as to reconstruct the label latent representation of the vertex and updating the label latent representations of the neighboring nodes using gradients resulting from application of a reconstruction loss. A classifier/regressor is trained using the node vector representations and the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.
First claim
Opening claim text (preview).
What is claimed is: 1. A method for learning latent representations of nodes in a personalization system using a graph-based machine learning framework, the method comprising: generating a graph representation containing the nodes based on input data; associating the nodes with labels; learning node vector representations by combining label latent representations from a vertex and neighboring nodes so as to reconstruct the label latent representation of the vertex and updating…
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Physics · mapped topic
Physics · mapped topic
Physics · mapped topic
- G16H50/70Primary
Physics · mapped topic
Physics · mapped topic
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Frequently asked questions
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- What does patent US11301774B2 cover?
- A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned by combining label latent representations from a vertex and n…
- Who is the assignee on this patent?
- Nec Europe Ltd, Nec Corp
- What technology area does this patent fall under?
- Primary CPC classification G16H50/70. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Tue Apr 12 2022 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 4 related publications on this page (citations in our corpus or others sharing the same primary CPC).