Personalization engine for building a dynamic classification dictionary

• US 9,619,467 B2
• Filed: 07/23/2012
• Issued: 04/11/2017
• Est. Priority Date: 06/27/2008
• Status: Active Grant

• Current Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Original Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Inventors: Pradhan, Tushar, Osborne, Thomas, Potter, John
• Primary Examiner(s): TRAN, ANHTAI V
• Application Number:
  US13/555,823
• Publication Number:
  US 20130191391A1
• Time in Patent Office: 1,723 Days
• Field of Search: None
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

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.

Claims

1. A computer-implemented method executed by one or more computing devices for building a dynamic classification dictionary, the method comprising:

extracting, by at least one of the one or more computing devices, one or more terms from information that characterizes a document;

applying, by at least one of the one or more computing devices, a first pattern matching rule to the one or more terms to identify one or more taxonomic nouns in the one or more terms, the first pattern matching rule including comparing sequences of consecutive words in the document with known terms and, if a match exists, merging matching consecutive words into a multi-word term and, if a match does not exist, assigning a single word term as the one or more taxonomic nouns;

applying, by at least one of the one or more computing devices, a second pattern matching rule to the one or more terms to determine at least one of a part-of-speech and a noun type associated with at least one taxonomic noun of the one or more taxonomic nouns, the second pattern matching rule being different from the first pattern matching rule; and

building, by at least one of the one or more computing devices, a dynamic classification dictionary based on the one or more taxonomic nouns and at least one of the part-of-speech and noun type associated with the at least one taxonomic noun.

2. The method of claim 1, wherein the information that characterizes the document comprises at least one of author-generated classification information regarding the document and a user-generated tag characterizing a portion of the document.

3. The method of claim 1, wherein the information that characterizes the document comprises one or more search terms that resulted in a user accessing the document.

4. The method of claim 1, wherein the information that characterizes the document comprises one or more attributes related to the manner in which a user accessed the document.

5. The method of claim 1, wherein the second pattern matching rule is a contextual pattern matching rule and wherein applying the second pattern matching rule comprises:

comparing the context of the at least one taxonomic noun to one or more known contexts which are indicative of at least one of a part-of-speech and a noun type, wherein the context of the at least one taxonomic noun includes terms surrounding the at least one taxonomic noun; and

marking the at least one taxonomic noun as being associated with at least one of a part-of-speech and a noun type corresponding to a matching context in the one or more known contexts.

6. The method of claim 1, wherein the second pattern matching rule includes weighing the one or more taxonomic nouns using a predetermined weighing scheme, wherein the weighing scheme accounts for systematic differences in predicted variability of taxonomic noun assignments if the assignments were repeatedly carried out.

7. An apparatus for building a dynamic classification dictionary, the apparatus comprising:

one or more processors; and

one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to;

extract one or more terms from information that characterizes a document;

apply a first pattern matching rule to the one or more terms to identify one or more taxonomic nouns in the one or more terms, the first pattern matching rule including comparing sequences of consecutive words in the document with known terms and, if a match exists, merging matching consecutive words into a multi-word term and, if a match does not exist, assigning a single word term as the one or more taxonomic nouns;

apply a second pattern matching rule to the one or more terms to determine at least one of a part-of-speech and a noun type associated with at least one taxonomic noun of the one or more taxonomic nouns, the second pattern matching rule being different from the first pattern matching rule; and

build a dynamic classification dictionary based on the one or more taxonomic nouns and at least one of the part-of-speech and noun type associated with the at least one taxonomic noun.

8. The apparatus of claim 7, wherein the information that characterizes the document comprises at least one of author-generated classification information regarding the document and a user-generated tag characterizing a portion of the document.

9. The apparatus of claim 7, wherein the information that characterizes the document comprises one or more search terms that resulted in a user accessing the document.

10. The apparatus of claim 7, wherein the information that characterizes the document comprises one or more attributes related to the manner in which a user accessed the document.

11. The apparatus of claim 7, wherein the second pattern matching rule is a contextual pattern matching rule and wherein the instructions that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to apply the second pattern matching rule further cause at least one of the one or more processors to:

comparing the context of the at least one taxonomic noun to one or more known contexts which are indicative of at least one of a part-of-speech and a noun type, wherein the context of the at least one taxonomic noun includes terms surrounding the at least one taxonomic noun; and

marking the at least one taxonomic noun as being associated with at least one of a part-of-speech and a noun type corresponding to a matching context in the one or more known contexts.

12. The apparatus of claim 7, wherein the second pattern matching rule is a contextual pattern matching rule and wherein the instructions that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to apply the second pattern matching rule further cause at least one of the one or more processors to:

weighing the one or more taxonomic nouns using a predetermined weighing scheme, wherein the weighing scheme accounts for systematic differences in predicted variability of taxonomic noun assignments if the assignments were repeatedly carried out.

13. At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more computing devices, cause at least one of the one or more computing devices to:

extract one or more terms from information that characterizes a document;

apply a first pattern matching rule to the one or more terms to identify one or more taxonomic nouns in the one or more terms, the first pattern matching rule including comparing sequences of consecutive words in the document with known terms and, if a match exists, merging matching consecutive words into a multi-word term and, if a match does not exist, assigning a single word term as the one or more taxonomic nouns;

apply a second pattern matching rule to the one or more terms to determine at least one of a part-of-speech and a noun type associated with at least one taxonomic noun of the one or more taxonomic nouns, the second pattern matching rule being different from the first pattern matching rule; and

build a dynamic classification dictionary based on the one or more taxonomic nouns and at least one of the part-of-speech and noun type associated with the at least one taxonomic noun.

14. The at least one non-transitory computer-readable medium of claim 13, wherein the information that characterizes the document comprises at least one of author-generated classification information regarding the document and a user-generated tag characterizing a portion of the document.

15. The at least one non-transitory computer-readable medium of claim 13, wherein the information that characterizes the document comprises one or more search terms that resulted in a user accessing the document.

16. The at least one non-transitory computer-readable medium of claim 13, wherein the information that characterizes the document comprises one or more attributes related to the manner in which a user accessed the document.

17. The at least one non-transitory computer-readable medium of claim 13, wherein the second pattern matching rule is a contextual pattern matching rule and wherein the instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to apply the second pattern matching rule further cause at least one of the one or more computing devices to:

compare the context of the at least one taxonomic noun to one or more known contexts which are indicative of at least one of a part-of-speech and a noun type, wherein the context of the at least one taxonomic noun includes terms surrounding the at least one taxonomic noun; and

mark the at least one taxonomic noun as being associated with at least one of a part-of-speech and a noun type corresponding to a matching context in the one or more known contexts.

18. The at least one non-transitory computer-readable medium of claim 13, wherein the instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to apply the second pattern matching rule further cause at least one of the one or more computing devices to:

weigh the one or more taxonomic nouns using a predetermined weighing scheme, wherein the weighing scheme accounts for systematic differences in predicted variability of taxonomic noun assignments if the assignments were repeatedly carried out.