SYSTEMS AND METHODS FOR RANKING COMMENTS

• US 20170140036A1
• Filed: 11/12/2015
• Published: 05/18/2017
• Est. Priority Date: 11/12/2015
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Owens, Erich James
• Granted Patent: US 10,630,632 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

Systems, methods, and non-transitory computer-readable media can train a model to define relatedness ratings for a plurality of terms. A posted content item and a comment to the posted content item are received. A relevance rating for the comment and the posted content item is determined based on the model. The comment is ranked among a plurality of comments based on the relevance rating.

Claims

1. A computer-implemented method comprising:

training, by a computing system, a model to define relatedness ratings for a plurality of terms, each relatedness rating indicative of a relatedness between two terms;

receiving, by the computing system, a posted content item;

receiving, by the computing system, a comment to the posted content item;

determining, by the computing system, a relevance rating for the comment and the posted content item based on the model; and

ranking, by the computing system, the comment among a plurality of comments based on the relevance rating.

2. The computer-implemented method of claim 1, further comprising defining a multi-dimensional space based on the model.

3. The computer-implemented method of claim 2, further comprising mapping the posted content item and the comment in the multi-dimensional space.

4. The computer-implemented method of claim 3, wherein determining a relevance rating for the comment and the posted content item comprises calculating a cosine similarity between the posted content item and the comment in the multi-dimensional space.

5. The computer-implemented method of claim 1, wherein training the model to define relatedness ratings for a plurality of terms comprises analyzing a plurality of pages on a social networking system for related terms.

6. The computer-implemented method of claim 1, wherein ranking the comment among a plurality of comments based on the relevance rating comprises determining whether the relevance rating satisfies a lower relevance rating threshold.

7. The computer-implemented method of claim 1, wherein ranking the comment among a plurality of comments based on the relevance rating comprises determining whether the relevance rating satisfies an upper relevance rating threshold.

8. The computer-implemented method of claim 7, wherein no ranking benefit is conferred by the relevance rating if the relevance rating exceeds the upper relevance rating threshold.

9. The computer-implemented method of claim 1, wherein ranking the comment among a plurality of comments based on the relevance rating comprises crediting an interaction rating based on the relevance rating.

10. The computer-implemented method of claim 1, wherein receiving a posted content item comprises analyzing a non-text posted content item and associating one or more terms with the non-text posted content item.

11. A system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to perform;

training a model to define relatedness ratings for a plurality of terms, each relatedness rating indicative of a relatedness between two terms;

receiving a posted content item;

receiving a comment to the posted content item;

determining a relevance rating for the comment and the posted content item based on the model; and

ranking the comment among a plurality of comments based on the relevance rating.

12. The system of claim 11, wherein the method further comprises defining a multi-dimensional space based on the model.

13. The system of claim 12, wherein the method further comprises mapping the posted content item and the comment in the multi-dimensional space.

14. The system of claim 13, wherein determining a relevance rating for the comment and the posted content item comprises calculating a cosine similarity between the posted content item and the comment in the multi-dimensional space.

15. The system of claim 11, wherein training the model to define relatedness ratings for a plurality of terms comprises analyzing a plurality of pages on a social networking system for related terms.

16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

training a model to define relatedness ratings for a plurality of terms, each relatedness rating indicative of a relatedness between two terms;

receiving a posted content item;

receiving a comment to the posted content item;

determining a relevance rating for the comment and the posted content item based on the model; and

ranking the comment among a plurality of comments based on the relevance rating.

17. The non-transitory computer-readable storage medium of claim 16, wherein the method further comprises defining a multi-dimensional space based on the model.

18. The non-transitory computer-readable storage medium of claim 17, wherein the method further comprises mapping the posted content item and the comment in the multi-dimensional space.

19. The non-transitory computer-readable storage medium of claim 18, wherein determining a relevance rating for the comment and the posted content item comprises calculating a cosine similarity between the posted content item and the comment in the multi-dimensional space.

20. The non-transitory computer-readable storage medium of claim 16, wherein training the model to define relatedness ratings for a plurality of terms comprises analyzing a plurality of pages on a social networking system for related terms.