Content Searching For Peer-To-Peer Collaboration

• US 20080077580A1
• Filed: 09/21/2007
• Published: 03/27/2008
• Est. Priority Date: 09/22/2006
• Status: Abandoned Application

• Current Assignee: LeapTag, Inc.
• Original Assignee: LeapTag, Inc.
• Inventors: Ozveren, Cuneyt, Kirazci, Ulas
• Application Number:
  US11/859,478
• Publication Number:
  US 20080077580A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04L 67/306 User profiles

Abstract

A system and method for searching content objects. The system includes a client, a cache, and a content searching application. The client is capable of accessing content objects over the internet. The cache is coupled to the client and configured to store at least a partial copy of object vectors representative of content in the content objects. The content searching application is coupled to the cache and configured to compare the object vectors to a user interest profile to find content similar to the user interest profile.

Claims

1. A system to search content objects, the system comprising:

a client capable of accessing content objects over the internet;

a cache coupled to the client, the cache to store at least a partial copy of object vectors representative of content in the content objects; and

a content searching application coupled to the cache, the content searching application to compare object vectors to a user interest profile to find content similar to the user interest profile.

2. The system of claim 1, wherein the object vector comprises a vector of numbers representative of a frequency of a superset of features potentially found in the content object.

3. The system of claim 1, wherein the content searching application compares at least a portion of the user interest profile to weighted averages for positive and negative example sets using a Bayesian algorithm.

4. The system of claim 1, wherein the content searching application compares at least a portion of the user interest profile to a maximally separating boundary between positive and negative example sets using a Support Vector Machine algorithm.

5. The system of claim 1, wherein the content searching algorithm compares at least a portion of the user interest profile to positive and negative objects using a Spectral Graph Theory algorithm to identify a boundary as a minimum cut problem.

6. The system of claim 1, wherein the user interest profile comprises a vector of numbers that identify the likes and dislikes of a user.

7. A computer program product comprising a computer useable storage medium to store a computer readable program that, when executed on a computer, causes the computer to perform operations for searching content, the operations comprising:

access content objects over the internet;

store at least a partial copy of object vectors representative of content in the content objects; and

compare the object vectors to a user interest profile to find content similar to the user interest profile.

8. The computer program product of claim 7, wherein the computer readable program, when executed on the computer, causes the computer to perform an operation to compare at least a portion of the user interest profile to weighted averages for positive and negative example sets using a Bayesian algorithm.

9. The computer program product of claim 7, wherein the computer readable program, when executed on the computer, causes the computer to perform an operation to compare at least a portion of the user interest profile to a maximally separating boundary between positive and negative example sets using a Support Vector Machine algorithm.

10. The computer program product of claim 7, wherein the computer readable program, when executed on the computer, causes the computer to perform an operation to compare at least a portion of the user interest profile to positive and negative objects using a Spectral Graph Theory algorithm to identify a boundary as a minimum cut problem.

11. A method for searching content, the method comprising:

accessing content objects over the internet;

storing at least a partial copy of object vectors representative of content in the content objects; and

comparing the object vectors to a user interest profile to find content similar to the user interest profile.

12. The method of claim 11, further comprising comparing at least a portion of the user interest profile to weighted averages for positive and negative example sets using a Bayesian algorithm.

13. The method of claim 11, further comprising comparing at least a portion of the user interest profile to a maximally separating boundary between positive and negative example sets using a Support Vector Machine algorithm.

14. The method of claim 11, further comprising comparing at least a portion of the user interest profile to positive and negative objects using a Spectral Graph Theory algorithm to identify a boundary as a minimum cut problem.