US 11,599,728 B1Granted

Semantic content clustering based on user interactions

AssigneeScribd, Inc.

InventorsMatthew Allen Strong Ross, Monique Alves Cruz

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Abstract

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Topic Engine. The Topic Engine captures a plurality of content identifier sequences. Each respective sequence represents an order at which a corresponding user account accessed content. The Topic Engine generates a plurality of clusters. Each cluster is associated with respective content identifiers appearing within a proximity to each other across the plurality of content identifier sequences of different user accounts. The Topic Engine obtains one or more sample content identifiers from at least one cluster via sampling the cluster. The Topic Engine extracts keywords from content represented by the one or more sampled content identifiers. The Topic Engine identifies a topic for the cluster based on the one or more extracted keywords.

Claims18

Dependent claims

A computer-implemented method, comprising:

capturing a plurality of content identifier sequences, each respective sequence representing an order at which a corresponding user account accessed content, wherein at least a subset of the plurality of content identifier sequences includes respective content identifier sequences that include at least a first content identifier and a second content identifier, each respective content identifier sequence in the subset further associated with a different user account;
generating a plurality of clusters, each cluster associated with respective content identifiers appearing within a proximity to each other across the plurality of content identifier sequences of different user accounts, wherein generating the plurality of clusters further comprises:
generating at least a first cluster associated at least with the first and the second content identifiers based on a recurring range of proximity of respective positions of the first and the second content identifiers appearing across the subset's content identifier sequences;
obtaining one or more sample content identifiers from at least one cluster via sampling the cluster;
extracting one or more keywords from content represented by the one or more sampled content identifiers;
identifying a topic for the cluster based on the one or more extracted keywords.

3 more elements

A system comprising one or more processors, and a non-transitory computer-readable medium including one or more sequences of instructions that, when executed by the one or more processors, cause the system to perform operations comprising:

capturing a plurality of content identifier sequences, each respective sequence representing an order at which a corresponding user account accessed content, wherein a first content identifier corresponds to first content provided by a first user, a second content identifier corresponds to second content provided by a second user, the first and second content comprising differing formats;
generating a plurality of clusters, each cluster associated with respective content identifiers appearing within a proximity to each other across the plurality of content identifier sequences of different user accounts;
obtaining one or more sample content identifiers from at least one cluster via sampling the cluster;
extracting one or more keywords from content represented by the one or more sampled content identifiers;
identifying a topic for the cluster based on the one or more extracted keywords.

2 more elements

A computer program product comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:

capturing a plurality of content identifier sequences, each respective sequence representing an order at which a corresponding user account accessed content;
generating a plurality of clusters, each cluster associated with respective content identifiers appearing within a proximity to each other across the plurality of content identifier sequences of different user accounts,
wherein generating the plurality of clusters comprises:
(i) generating a vector representation of each content identifier that appears in the plurality of content identifier sequences, the respective vector representation indicating a position in dimensional space;
(ii) generating a first set of clusters, each cluster in the first set comprising a density of content identifiers positioned according to corresponding vector representation;
and
(iii) generating a second set of clusters, each cluster in the second set comprising a diffuse collection of one or more content identifiers positioned according to corresponding vector representation;
obtaining one or more sample content identifiers from at least one cluster via sampling the cluster;
extracting one or more keywords from content represented by the one or more sampled content identifiers;
identifying a topic for the cluster based on the one or more extracted keywords.

7 more elements

Dependencies are inferred from the claim text.