Method and apparatus for clustering news online content based on content freshness and quality of content source

• US 9,361,369 B1
• Filed: 07/13/2012
• Issued: 06/07/2016
• Est. Priority Date: 09/20/2002
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Bharat, Krishna, Curtiss, Michael, Schmitt, Michael
• Primary Examiner(s): Paula, Cesar
• Assistant Examiner(s): Kulikowski, Konrad
• Application Number:
  US13/548,930
• Time in Patent Office: 1,425 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 40/143 Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: Y10S 707/99937 Sorting
• CPC Class Codes: Y10S 707/99953 Recoverability

Abstract

Methods and apparatus are described for scoring documents in response, in part, to parameters related to the document, source, and/or cluster score. Methods and apparatus are also described for scoring a cluster in response, in part, to parameters related to documents within the cluster and/or sources corresponding to the documents within the cluster. In one embodiment, the invention may identify the source; detect a plurality of documents published by the source; analyze the plurality of documents with respect to at least one parameter; and determine a source score for the source in response, in part, to the parameter. In another embodiment, the invention may identify a topic; identify a plurality of clusters in response to the topic; analyze at least one parameter corresponding to each of the plurality of clusters; and calculate a cluster score for each of the plurality of clusters in response, in part, to the parameter.

Claims

1. A method comprising:

identifying, by one or more processors, online content,

the online content being associated with a document;

calculating, by one or more processors, a first score for the online content, based on a measure of freshness of the online content,

the online content including content relating to an event, and

the measure of freshness being based on a first time when the event occurred and a second time when the online content was published;

determining, by the one or more processors, a second score for the online content based on a quality of a source that published the online content,

the source being an entity that publishes documents;

ranking, by the one or more processors, the online content based on the first score and the second score; and

providing, by the one or more processors, the ranked online content for display.

2. The method of claim 1, where the measure of freshness is based on an amount of time between the first time and the second time.

3. The method of claim 1, where the measure of freshness is based on a frequency in which the source of the online content publishes new online content in a particular time period.

4. The method of claim 1, where the second score is based on circulation statistics of the source.

5. The method of claim 1, where the second score is based on a number of views of the online content that occur within a particular time frame.

6. The method of claim 1, further comprising:

determining whether duplicates of the online content exist that were published before the online content was published by the source; and

calculating the second score after determining whether the duplicates of the online content exist.

7. The method of claim 1, further comprising:

ranking the source, among other sources, based on the second score.

8. A system comprising:

one or more processors to;

identify online content,

the online content being associated with a document;

calculate a first score for the online content based on a measure of freshness of the online content,

the online content including content relating to an event, and

the measure of freshness being based on a first time when the event occurred and a second time when the online content was published;

determine a second score for the online content based on a quality of a source that published the online content,

the source including an entity that publishes documents;

rank the online content based on the first score and the second score; and

provide the ranked online content for display.

9. The system of claim 8, where the measure of freshness is based on an amount of time between the first time and the second time.

10. The system of claim 8, where the measure of freshness is based on a frequency in which the source of the online content publishes new online content in a particular time period.

11. The system of claim 8, where the second score is based on circulation statistics of the source.

12. The system of claim 8, where the second score is based on a number of views, of the online content that occur within a particular time frame.

13. The system of claim 8, where the one or more processors are further to:

determine whether duplicates of the online content exist that were published before the online content was published by the source; and

calculate the second score after determining whether the duplicates of the online content exist.

14. The system of claim 8, where the one or more processors are further to:

rank the source, among other sources, based on the second score.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to;

identify online content,

the online content being associated with a document;

calculate a first score for the online content based on a measure of freshness of the online content,

the online content including content relating to an event, and

the measure of freshness being based on a first time when the event occurred and a second time when the online content was published;

determine a second score for the online content based on a quality of a source that published the online content,

the source including an entity that publishes documents;

rank the online content based on the first score and the second score; and

provide the ranked online content for display.

16. The non-transitory computer-readable medium of claim 15, where the measure of freshness is based on an amount of time between the first time and the second time.

17. The non-transitory computer-readable medium of claim 15, where the measure of freshness is based on a frequency in which the source of the online content publishes new online content in a particular time period.

18. The non-transitory computer-readable medium of claim 15, where the second score is based on circulation statistics of the source.

19. The non-transitory computer-readable medium of claim 15, where the second score is based on a number of views, of the online content that occur within a particular time frame.

20. The non-transitory computer-readable medium of claim 15, where the instructions further comprise:

one or more instructions to determine whether duplicates of the online content exist that were published before the online content was published by the source; and

one or more instructions to calculate the second score after determining whether the duplicates of the online content exist.