PREDICTING A SEARCH ENGINE RANKING SIGNAL VALUE

• US 20180157758A1
• Filed: 12/05/2016
• Published: 06/07/2018
• Est. Priority Date: 12/05/2016
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Arrizabalaga, Javier Spagnolo, Nuhn, Malte, Le, Quoc V., Duckworth, Daniel, Heiler, Matthias
• Granted Patent: US 10,324,993 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for augmenting search engine index that indexes resources from a collection of resources. In one aspect, a method of augmenting a first search engine index that indexes resources from a first collection of resources includes the actions of identifying a first resource, in the first collection of resources, that is indexed in the first search engine index for which a value of a search engine ranking signal is not available, wherein a search engine uses values of the search engine ranking signal in ranking resources in response to received search queries; processing text from the first resource using a machine learning model, the machine learning model being configured to: process the text to predict a value of the search engine ranking signal for the first resource; and updating the first search engine index by associating the predicted value of the search engine ranking signal with the first resource in the first search engine index.

Claims

1. A method of augmenting a first search engine index that indexes resources from a first collection of resources, the method comprising:

identifying a first resource, in the first collection of resources, that is indexed in the first search engine index for which a value of a search engine ranking signal is not available, wherein a search engine uses values of the search engine ranking signal in ranking resources in response to received search queries;

processing text from the first resource using a machine learning model, the machine learning model being configured to;

process the text to predict a value of the search engine ranking signal for the first resource; and

updating the first search engine index by associating the predicted value of the search engine ranking signal with the first resource in the first search engine index.

2. The method of claim 1, further comprising:

identifying a plurality of second resources from a second search engine index that indexes a second collection of resources, each of the second resources being associated in the second search engine index with a respective value of the search engine ranking signal for the second resource;

generating training data that includes, for each of the plurality of second resources;

text of the second resource, and

the respective value of the search engine ranking signal for the second resource; and

training the machine learning model on the training data.

3. The method of claim 2, wherein the second collection of resources is a collection of Internet resources.

4. The method of claim 3, wherein the first collection of resources is different from the second collection of resources.

5. The method of claim 4, wherein the first collection of resources is a collection of entity-specific resources.

6. The method of claim 1, wherein the predicted value of the search engine ranking signal for the first resource includes one or more search queries that users would submit to the search engine to search for the first resource.

7. The method of claim 1, wherein the machine learning model comprises:

an encoder neural network configured to process the text of the first resource to generate an encoded representation of the first resource; and

a first decoder neural network configured to generate the predicted value of the search engine ranking signal using the encoded representation of the first resource.

8. The method of claim 7, wherein a value of a second search engine ranking signal also used by the search engine in ranking resources in response to received search queries is also not available for the first resource, and wherein the method further comprises:

processing the encoded representation of the first resource using a second decoder neural network configured to generate a predicted value of the second search engine ranking signal using the encoded representation of the first resource.

9. A method of augmenting a search engine index that indexes resources from a collection of resources, the method comprising:

identifying a plurality of first resources from a first search engine index that indexes a first collection of resources, each of the first resources being associated in the first search engine index with a respective value of the search engine ranking signal for the first resource;

generating training data that includes, for each of the plurality of first resources;

text of the first resource, and

the respective value of the search engine ranking signal for the first resource;

training a machine learning model on the training data, wherein the machine learning model is configured to;

receive text of a resource, and

process the text of the resource to generate a predicted value of the search engine ranking signal for the resource.

10. The method of claim 9, further comprising:

identifying a second resource, in a second collection of resources, that is indexed in a second search engine index for which a value of a search engine ranking signal is not available, wherein a search engine uses values of the search engine ranking signal in ranking resources in response to received search queries;

processing text from the second resource using the trained machine learning model, the trained machine learning model being configured to;

process the text to predict a value of the search engine ranking signal for the second resource; and

updating the second search engine index by associating the predicted value of the search engine ranking signal with the second resource in the second search engine index.

11. The method of claim 10, wherein the first collection of resources is a collection of Internet resources.

12. The method of claim 11, wherein the second collection of resources is different from the first collection of resources.

13. The method of claim 12, wherein the second collection of resources is a collection of entity-specific resources.

14. The method of claim 10, wherein the predicted value of the search engine ranking signal for the second resource includes one or more search queries that users would submit to the search engine to search for the second resource.

15. The method of claim 10, wherein the machine learning model comprises:

an encoder neural network configured to process the text of the second resource to generate an encoded representation of the second resource; and

a second decoder neural network configured to generate the predicted value of the search engine ranking signal using the encoded representation of the second resource.

16. The method of claim 15, wherein a value of a first search engine ranking signal also used by the search engine in ranking resources in response to received search queries is also not available for the second resource, and wherein the method further comprises:

processing the encoded representation of the second resource using a first decoder neural network configured to generate a predicted value of the first search engine ranking signal using the encoded representation of the second resource.