US 10,380,129 B2Granted

Automated measurement of content quality

AssigneeMicrosoft Technology Licensing, LLC

InventorsDavid Golland, Eric Huang, Patrick Chase, Alexandre Patry, Shakti Dhirendraji Sinha

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Abstract

A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Claims20

Dependent claims

A system comprising:

a processor, and a memory including instructions, which when executed by the processor, cause the processor to:
generate a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generate an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generate a second graph comprising the user node and the article node;
generate an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculate a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculate a second authority score for the article node within the second graph based on a hub score of the user node in the second graph;
and
calculate a quality score for the article based on a weighted average of the first authority score and the second authority score;
rank the article among other articles based on the quality score;
and
display the article in a graphical user interface based on the ranking.

9 more elements

A method comprising:

using one or more computer processors:
generating a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generating an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generating a second graph comprising the user node and the article node;
generating an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculating a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculating a second authority score for the article node within the second graph based on a hub score of the user node in the second graph;
and
calculating a quality score for the article based on a weighted average of the first authority score and the second authority score;
ranking the article among other articles based on the quality score;
and
displaying the article in a graphical user interface based on the ranking.

9 more elements

A machine-readable medium not having any transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:

generating a first graph comprising a user node and an article node, the user node uniquely corresponds to a user and the article node uniquely corresponds to an article;
generating an edge between the user node and the article node in the first graph based on a first action type by the user in association with the article;
generating a second graph comprising the user node and the article node;
generating an edge between the user node and the article node in the second graph based on a second action type by the user in association with the article;
calculating a first authority score for the article node within the first graph based on a hub score of the user node in the first graph;
calculating a second authority score for the article node within the second graph based on a hub score of the user node in the second graph;
and
calculating a quality score for the article based on a weighted average of the first authority score and the second authority score;
ranking the article among other articles based on the quality score;
and
displaying the article in a graphical user interface based on the ranking.

8 more elements

Dependencies are inferred from the claim text.