Cbs Interactive, Inc.

John F Potter

Tushar Pradhan

Thomas Osborne

Mark Kaufman

William W Graham, Jr

Stephanie Lone

Keric Donnelly

A dynamic classification dictionary is built for use in profiling and targeting users for additional relevant content. Behavioral data is gathered from user activity, and user documents and actions are categorized. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to the manner in which the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated into a composite set of taxonomic nouns, and the dynamic classification dictionary is built by storing the composite set of taxonomic nouns.

Personalization engine for building a dynamic classification dictionary

A dynamic classification dictionary is built for use in profiling and targeting users for additional relevant content. Behavioral data is gathered from user activity, and user documents and actions are categorized. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to the manner in which the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated into a composite set of taxonomic nouns, and the dynamic classification dictionary is build by storing the composite set of taxonomic nouns.

Personalization engine for characterizing a document

Personalization engine for assigning a value index to a user

A computer system for generating a user data store and analyzing the user data store comprises a memory and a processing circuit. The memory is configured to store a user data store comprising a plurality of graphs, each graph having a user identity node. The processing circuit is configured to, for each of the plurality of graphs: receive asset data representing an asset that a user has interacted with on a website; generate a first node representing the asset data and a first link coupling the first node to the user identity node of the graph; store the first node and first link as part of the graph; receive usage data representing user interaction with a website; generate a second node representing the usage data and a second link coupling the second node to the user identity node of the graph, wherein at least one of the first node and second node further represents opinion data for a product or evaluation data for a product; and store the second node and second link as part of the graph.

User data store

Unstructured electronic documents are classified for profiling and targeting users for additional relevant content. Behavioral data is gathered from user activity, and user documents and actions are categorized. Profile information is combined with collaborative and editorial data to provide users with credible information regarding products. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to how the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated to determine term vectors representing the document, and the document is categorized using the term vectors, the taxonomic nouns, or the author-generated classification.

Personalization engine for classifying unstructured documents

Users of electronic documents are classified for profiling and targeting of additional relevant content. Behavioral data is gathered from user registration information and user activity, and user documents and actions are categorized. Registration information is combined with collaborative and editorial data to provide user profile information. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to how the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated to determine a composite set of taxonomic nouns, and the user is categorized using the taxonomic nouns, and/or the author-generated classification.

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Palo Alto, CA, United States of America

John F Potter