User Interactive Precision Targeting Principle

• US 20080301118A1
• Filed: 06/01/2007
• Published: 12/04/2008
• Est. Priority Date: 06/01/2007
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Chien, Shu-Yao, Seth, Amitabh, John, George H., Rochnik, Nikolai
• Granted Patent: US 7,882,111 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing

Abstract

A method of determining content relevance for a user sets a user preference, which is related to a first area of content. The method calculates a set of scores, by using a combination, of the user preference, affinity data, and a parametric weight. The method organizes the content by using the set of scores, such that the organization of the content has a desirable relationship to the user, and recommends the selected content. Preferably, the method precomputes the affinity data and/or the parametric weight to generate and store the precompiled data for later retrieval. The affinity data describes a relationship between a first item of content and a second item of content, and the parametric weight describes an attribute of the second item. Additional embodiments include a system implementation and computer readable medium.

Claims

1. A method of determining content relevance for a user, the method comprising:

setting a user preference, the user preference related to a first area;

calculating a set of scores, by using a combination of;

the user preference,

affinity data, and

a parametric weight;

organizing the content by using the set of scores, the organization of the content having a desirable relationship to the user; and

determining a recommendation from the organized content.

2. The method of claim 1, further comprising:

precomputing one of the affinity data and the parametric weight to generate precomputed data;

storing the precomputed data; and

retrieving the precomputed data.

3. The method of claim 1, wherein the affinity data comprises a relationship between a first item of content and a second item of content, wherein the parametric weight describes an attribute of the second item.

4. The method of claim 1, wherein the parametric weight comprises at least one of:

popularity and similarity.

5. The method of claim 1, wherein the content is organized by using a tree structure, wherein the parametric weight comprises tree distance.

6. The method of claim 1, further comprising:

receiving the user preference from a user, wherein the user applies a preference by using an interactive user interface.

7. The method of claim 1, further comprising:

storing the user preference, and

retrieving the user preference, at a later time.

8. The method of claim 1, wherein the user preference is set by using information retrieved from at least one of:

an area of content for a particular user, wherein the user sets the user preference;

a set of demographically similar users, wherein the user has little accumulated data, and the preference setting occurs without the need of user action.

9. The method of claim 1, wherein the set of scores comprises relevance scores.

10. The method of claim 1, further comprising:

storing the calculated set of scores; and

retrieving the calculated set of scores;

thereby

customizing for the particular user, a relevance metric for each of several items of content.

11. The method of claim 1, further comprising:

selecting content by using the calculated set of scores, and

presenting the recommended content to the user,

wherein the recommending comprises a push technology.

14. The method of claim 1, further comprising updating one of:

the user preference, the affinity data, the, parametric weight, and the set of scores, wherein the updating comprises one or more of an automated process, a periodic process, and a real time process.

12. (canceled)

13. (canceled)

15. (canceled)

16. A system for determining relevance of content, the system comprising:

a user interface for receiving a user preference;

a relevance engine comprising a parametric model;

the relevance engine for calculating a set of scores based on at least one of;

the user preference;

affinity data, and

a parametric weight;

a profile manager for organizing one of the user preference and the set of scores; and

a data storage for use by the profile manager to store and retrieve one of the user preference and the set of scores.

17. The system of claim 16, further comprising:

a network comprising a plurality of content items distributed throughout the network; and

a first device coupled to the network, the first device comprising the user interface for interacting with the network; and

a node coupled to the network, the node comprising the relevance engine.

18. The system of claim 16, wherein the relevance engine is configured for organizing content by using the set of scores, the selected content having a desirable relationship to the user.

19. The system of claim 16, further comprising a precomputation module for precomputing one of the affinity data and the parametric weight to generate precomputed data, wherein the precomputed data is available to the relevance engine.

20. The system of claim 16, wherein the affinity data comprises a relationship between a first item of content and a second item of content, wherein the parametric weight describes an attribute of the second item.

21. The system of claim 16, wherein the parametric weight comprises at least one of:

popularity and similarity.

22. The system of claim 16, wherein the content is organized by using a tree structure, wherein the parametric weight comprises tree distance.

23. The system of claim 16, further comprising a data path coupling the interactive user interface to the relevance engine, wherein the relevance engine is configured to output, by using a mathematical operation, the set of scores based on the user preference received via the user interface.

24. The system of claim 16, wherein the profile manager is configured to:

store the user preference, and retrieve the user preference, by using the data storage; and

provide a user preference setting to a particular user based on information from a group of similar users.

26. The system of claim 16, wherein the set of scores comprises relevance scores.

27. The system of claim 16, further comprising a user profile, the user profile comprising the calculated set of scores, wherein the profile manager is configured to store the user profile and retrieve the user profile.

28. The system of claim 16, further comprising an update module for updating one of:

the user preference, the affinity data, the parametric weight, and the set of scores, wherein the updating comprises one or more of an automated, a periodic, and a real time process.

25. (canceled)

29. (canceled)

30. A computer readable medium storing a program for determine content relevance, the program comprising instructions for:

setting a user preference, the user preference related to a first area of content;

calculating a set of scores, by using a combination of;

the user preference,

affinity data, and

a parametric weight;

organizing the content by using the set of scores, the organization of the content having a desirable relationship to the user; and

determining a recommendation from the organized content.