METHOD AND SYSTEM FOR CHARACTERIZING WEB CONTENT

• US 20110035378A1
• Filed: 08/10/2009
• Published: 02/10/2011
• Est. Priority Date: 08/10/2009
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Scholz, Martin B., Rajaram, Shyam Sundar, Lukose, Rajan
• Granted Patent: US 9,213,767 B2
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

An exemplary embodiment of the present invention provides a method of processing Web activity data. The method includes obtaining a database of Website organizational data. The method also includes generating a data structure from the database of Website organizational data comprising an Item identifier and a Website category corresponding to the item identifier. The method also includes generating a reduced-rank classification structure from the data structure, the reduced-rank classification structure including a category grouping corresponding to one or more of the Website categories.

Claims

1. A method of processing Web activity data, comprising:

obtaining a database of Website organizational data;

generating a data structure from the database of Website organizational data, the data structure including an Item identifier and a Website category corresponding to the item identifier; and

generating a reduced-rank classification structure from the data structure, the reduced-rank classification structure including a category grouping corresponding to one or more of the Website categories.

2. The method of claim 1, wherein the Website category comprises a unit of a Website'"'"'s taxonomy or a tag.

3. The method of claim 1, wherein generating the data structure comprises generating a matrix comprising item identifiers along a first axis and Website categories along a second axis.

4. The method of claim 3, wherein generating the data structure comprises filling the matrix depending on the level of certainty that a specific item identifier corresponds with a specific Website category.

5. The method of claim 1, wherein generating the reduced-rank classification structure comprises generating a matrix decomposition or a singular-value decomposition of the data structure.

6. The method of claim 1, further comprising generating an approximation of the Website organizational data based on the reduced-rank classification structure.

7. The method of claim 6, wherein generating an approximation of the Website organizational data comprises performing a sparse regression of the reduced-rank classification structure.

8. The method of claim 1, wherein the reduced-rank classification structure is used to generate a user profile.

9. The method of claim 1, wherein the reduced-rank classification structure represents a set of vectors corresponding to positions within a latent vector space.

10. The method of claim 9, comprising mapping a new Website'"'"'s organizational data to the latent space of the reduced-rank classification structure.

11. A computer system, comprising:

a processor that is configured to execute machine-readable instructions;

a storage device that is configured to store data, the data comprising a reduced-rank classification structure that includes category groupings; and

a memory device that stores instructions that are executable by the processor, the instructions comprising;

a feature extractor configured to extract Website-specific categories corresponding to an item included in a database of Web activity data of a user ID;

a category identifier configured to identify a category grouping from the reduced-rank classification structure that corresponds with the Website-specific categories; and

a profile builder configured to add the category grouping to a user profile.

12. The computer system of claim 11, wherein the reduced-rank classification structure comprises a singular-value decomposition of a matrix of Website items and Website categories.

13. The computer system of claim 12, wherein the Website categories correspond to a taxonomy or tag structure associated with each of the Website items.

14. The computer system of claim 11, wherein the category identifier is configured to map the Website-specific item and categories to a latent vector space corresponding to the reduced-rank classification structure.

15. The computer system of claim 14, wherein the category identifier is configured to identify a category grouping based on a proximity of the Website-specific item and the categories to the a category grouping within the latent vector space.

16. The computer system of claim 11, comprising a taxonomy generator configured to generate an approximation of a Website-specific taxonomy or tag structure based on the reduced-rank classification structure.

17. The computer system of claim 16, wherein the taxonomy generator is configured to generate the approximation of the Website-specific taxonomy via sparse regression of the reduced-rank classification structure.

18. A tangible, computer-readable medium, comprising code configured to direct a processor to:

extract Website-specific information from a Website corresponding to an item featured by the Website;

identify a category grouping in a reduced-rank classification structure that corresponds with the Website-specific information; and

add the category grouping to a user profile.

19. The tangible, computer-readable medium of claim 18, comprising code configured to direct the processor to generate an approximation of a Website-specific taxonomy or tag structure via sparse regression of the reduced-rank classification structure.

20. The tangible, computer-readable medium of claim 18, wherein the category identifier is configured to direct the processor to identify a category grouping based on the proximity of the Website-specific information to the category grouping within the latent vector space.