TECHNIQUES TO FILTER MEDIA CONTENT BASED ON ENTITY REPUTATION

• US 20090254499A1
• Filed: 04/07/2008
• Published: 10/08/2009
• Est. Priority Date: 04/07/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deyo, Roderic C.
• Granted Patent: US 8,200,587 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem operative to manage an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may comprise, among other elements, a data collection module operative to collect reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module communicatively coupled to the data collection module, the feature manager module operative to extract a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module communicatively coupled to the feature manager module, the reputation scoring module operative to generate an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed.

Claims

1. A method, comprising:

collecting reputation information for an entity from a selected set of multiple reputation sources;

extracting a set of reputation features from the reputation information; and

generating an entity reputation score based on the reputation features using a machine learning algorithm.

2. The method of claim 1, comprising collecting reputation information representing uni-directional encounter information or bi-directional encounter information between the entity and other entities.

3. The method of claim 1, comprising generating the entity reputation score using a supervised machine learning algorithm.

4. The method of claim 1, comprising generating the entity reputation score using an unsupervised machine learning algorithm.

5. The method of claim 1, comprising generating the entity reputation score based on the reputation features using an inductive predictive algorithm to perform contextual encounter-based reputation scoring to predict whether a random entity will have a positive experience.

6. The method of claim 1, comprising receiving a request for the entity reputation score from a content server for use in determining whether to allow the entity to submit media content to the content server.

7. The method of claim 1, comprising sending the entity reputation score to a content server for use in filtering media content submitted by the entity.

8. The method of claim 1, comprising filtering media content submitted by the entity based on the entity reputation score.

9. The method of claim 1, comprising granting permission to accept media content by a content server from the entity when the entity reputation score is greater than a defined threshold value, and denying permission to accept the media content when the entity reputation score is lesser than the defined threshold value.

10. An article comprising a storage medium containing instructions that if executed enable a system to:

collect reputation information for an entity from a selected set of multiple reputation sources;

extract a set of reputation features from the reputation information; and

generate an entity reputation score based on the reputation features using a machine learning algorithm.

11. The article of claim 10, further comprising instructions that if executed enable the system to collect reputation information representing uni-directional encounter information or bidirectional encounter information between the entity and other entities.

12. The article of claim 10, further comprising instructions that if executed enable the system to generate the entity reputation score using a supervised machine learning algorithm or an unsupervised machine learning algorithm.

13. The article of claim 10, further comprising instructions that if executed enable the system to generate the entity reputation score based on the reputation features using an inductive predictive algorithm to perform contextual encounter-based reputation scoring to predict whether a random entity will have a positive experience.

14. The article of claim 10, further comprising instructions that if executed enable the system to filter media content submitted by the entity based on the entity reputation score.

15. The article of claim 10, further comprising instructions that if executed enable the system to grant permission to accept media content by a content server from the entity when the entity reputation score is greater than a defined threshold value, and denying permission to accept the media content when the entity reputation score is lesser than the defined threshold value.

16. An apparatus, comprising:

a reputation subsystem operative to manage an entity reputation score for an entity, the reputation subsystem comprising a reputation manager component and a reputation input/output component, the reputation manager component comprising;

a data collection module operative to collect reputation information for an entity from a selected set of multiple reputation sources;

a feature manager module communicatively coupled to the data collection module, the feature manager module operative to extract a selected set of reputation features from the reputation information; and

a reputation scoring module communicatively coupled to the feature manager module, the reputation scoring module operative to generate an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm.

17. The apparatus of claim 16, comprising a content filter to receive the entity reputation score, compare the entity reputation score with a defined reputation threshold score to find an entity result value, and filter media content based on the entity result value.

18. The apparatus of claim 16, the reputation information representing uni-directional encounter information or bi-directional encounter information between the entity and other entities.

19. The apparatus of claim 16, the machine learning algorithm comprising an inductive predictive algorithm to perform contextual encounter-based reputation scoring to predict whether a random entity will have a positive experience.

20. The apparatus of claim 16, comprising a reputation service node having a computing system, the computing system comprising a memory to store program instructions for the reputation subsystem, and a processor coupled to the memory to execute program instructions for the reputation subsystem.