US 2019/0050750 A1Published application

Deep and wide machine learned model for job recommendation

AssigneeLinkedln Corporation

InventorsBenjamin Hoan Le, Saurabh Kataria, Nadia Fawaz, Aman Grover, Guoyin Wang

Life of this patent

Estimating the term…

Loading patent…

Abstract

In an example, features in a boosting decision tree model are initialized to zero, the boosting decision tree model located in a GLMM and connected to a deep neural network collaborative filtering model via a prediction layer. While the features in the boosting decision tree model remain zero, the deep neural network collaborative filtering model is trained. One or more trees in the boosting decision tree model are boosted using logits produced by the training of the deep neural network collaborative filtering model as a margin. The prediction layer is trained using features from the deep neural network collaborative filtering model and features from the boosting decision tree model. It is then determined whether a set of convergence criteria is met. If not, then the deep neural network collaborative filtering model is retrained using the features and the process is repeated until the set of convergence criteria is met.

Claims20

Dependent claims

A system comprising:

a memory storing a generalized linear mixed model (GLMM), the GLMM comprising a deep neural network collaborative filtering model and a boosting decision tree model connected via a prediction layer;
and
a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to:
initialize features in the boosting decision tree model to zero;
while the features in the boosting decision tree model remain zero, train the deep neural network collaborative filtering model;
boost one or more trees in the boosting decision tree model using logits produced by the training of the deep neural network collaborative filtering model as a margin;
train the prediction layer using features from the deep neural network collaborative filtering model and features from the boosting decision tree model;
determine whether a set of convergence criteria is met;
and
in response to a determination that the set of convergence criteria is not met:
retrain the deep neural network collaborative filtering model using the features in the boosting decision tree model;
and
repeat the boosting, training, determining, and retraining until the set of convergence criteria is met.

10 more elements

A computerized method comprising:

initializing features in a boosting decision tree model to zero, the boosting decision tree model located in a GLMM and connected to a deep neural network collaborative filtering model via a prediction layer;
while the features in the boosting decision tree model remain zero, training the deep neural network collaborative filtering model;
boosting one or more trees in the boosting decision tree model using logits produced by the training of the deep neural network collaborative filtering model as a margin;
training the prediction layer using features from the deep neural network collaborative filtering model and features from the boosting decision tree model;
determining whether a set of convergence criteria is met;
and
in response to a determination that the set of convergence criteria is not met:
retraining the deep neural network collaborative filtering model using the features in the boosting decision tree model;
and
repeating the boosting, training, determining, and retraining until the set of convergence criteria is met.

7 more elements

A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:

initializing features in a boosting decision tree model to zero, the boosting decision tree model located in a GLMM and connected to a deep neural network collaborative filtering model via a prediction layer;
while the features in the boosting decision tree model remain zero, training the deep neural network collaborative filtering model;
boosting one or more trees in the boosting decision tree model using logits produced by the training of the deep neural network collaborative filtering model as a margin;
training the prediction layer using features from the deep neural network collaborative filtering model and features from the boosting decision tree model;
determining whether a set of convergence criteria is met;
and
in response to a determination that the set of convergence criteria is not met:
retraining the deep neural network collaborative filtering model using the features in the boosting decision tree model;
and
repeating the boosting, training, determining, and retraining until the set of convergence criteria is met.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/674,968Filed 11 Aug 2017
Published: 14 Feb 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification20 codes

G06N 3/08
Neural networks: learning methods
G06F 3/0482
Based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like…:…
G06F 16/9024
Indexing; Data structures therefor; Storage structures: graphs; Linked lists
G06F 16/906
Details of database functions independent of the retrieved data types: clustering; Classification

Family

US 10,990,899 B2Related patent
US 2019/0050750 A1This patent

From the US filing history. Foreign family members aren’t listed.

Timeline

Citations

Open elsewhere

Building an agent or pulling many patents? Use the API or MCP server instead of scraping pages. Get an API key