TRAINING ITEM RECOGNITION VIA TAGGING BEHAVIOR

• US 20080082466A1
• Filed: 12/20/2006
• Published: 04/03/2008
• Est. Priority Date: 09/28/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Cheng, Lili, MacLaurin, Matthew B., Meijer, Henricus Johannes Maria, Ozzie, Raymond E., Flake, Gary W., Zaner-Godsey, Melora, Brumme, Christopher W., Blinn, Arnold N., Gounares, Alexander G., Glasser, Daniel S., Bergstraesser, Thomas F., Mishra, Debi P., Connolly, Michael
• Granted Patent: US 7,672,909 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems and methods that recognize items based on aggregated tagging behavior of users regarding the items. Individual users can establish relationships among tagged items and the system analyzes aggregate of such established relationships, to recognize the item and/or infer additional information regarding the items (e.g., wisdom of crowd such as metadata annotations, relevance ranking, and the like). Subsequently, search engine crawlers can be supplied with such additional information (e.g., extra metadata for reverse link search tables) to facilitate enterprise management and search.

Claims

1. A machine learning system that analyzes tagging behavior, comprising:

a recognition component that recognizes items based on aggregate of user tagging behavior associated with the items; and

a storage medium that stores recognized items.

2. The machine learning system of claim 1, the recognition component further comprising generic classifiers that classify tagged items based on aggregate of user tagging behavior.

3. The machine learning system of claim 1, the recognition component further comprising user specific classifier that is based on user demographics.

4. The machine learning system of claim 2 further comprising a search engine that searches for items based on classification.

5. The machine learning system of claim 4 further comprising a Dewey Decimal System that facilitates data organization based on hierarchical tags.

6. The machine learning system of claim 1 further comprising a training model for determination of tagging trends.

7. The machine learning system of claim 3 further comprising an artificial intelligence component that facilitates trend establishment among tagged items, the tagged items including at least one of digital photographs, e-mails, web pages, and data files

8. The machine learning system of claim 1, further comprising a display component having a tag controller that sets thresholds for trend establishment.

9. The machine learning system of claim 4 further comprising a Bayesian classifier.

10. The machine learning system of claim 1 further comprising a user feed back component that collects user feedback associated with tagging behavior of items.

11. A method of inferring tagging behavior comprising:

collecting relationships established by a plurality of users when tagging items; and

recognizing the items based on tagging behavior of users.

12. The method of claim 11 further comprising evaluating a trustworthiness of a user based on relationships established thereby.

13. The method of claim 11 further comprising inferring relationships among tags.

14. The method of claim 11 further comprising organizing items based on tagging behavior of the plurality of users.

15. The method of claim 11 further comprising browsing data based on algorithmically-deduced relationships between items.

16. The method of claim 11 further comprising searching for items based on the tagging behavior of users.

17. The method of claim 11 further comprising training a machine learning system for inferring tagging behavior of users.

18. The method of claim 11 further comprising automatically generating possible tagging criteria.

19. The method of claim 11 further comprising incorporating inferred tagging criteria in taxonomy applications.

20. A machine learning system that analyzes tagging behavior, comprising:

means for recognizing items based on aggregate of user tagging behavior regarding the items; and

means for searching for items based on aggregate of the user tagging behavior.