Integrated interest and expertise-based discovery system and method

• US 8,600,926 B2
• Filed: 11/14/2011
• Issued: 12/03/2013
• Est. Priority Date: 03/29/2011
• Status: Active Grant

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Moneypenny, Naomi Felina, Flinn, Steven Dennis
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US13/295,414
• Publication Number:
  US 20120254099A1
• Time in Patent Office: 750 Days
• Field of Search: None
• US Class Current: 706/52
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

An integrated interest and expertise-based discovery system and method infers interest and expertise levels of users and generates recommendations for delivery to the users in accordance with the inferred interest and expertise levels. The inferences of expertise may be relative to other users and may be combined with expertise calibration information in generating expected expertise levels for users with respect to one or more topics. The generation of recommendations may be informed by the degree of inferred interest in topical areas, by the comparison of expertise levels among users, and by information regarding the user expertise levels that are appropriate with respect to specific items of content. Explanations may be provided to recommendation recipients as to why they received recommendations.

Claims

1. A computer-implemented method, comprising:

accessing a first plurality of values, wherein each of the first plurality of values is based, at least in part, on an inference of a first user'"'"'s interest level with respect to a topic;

accessing a second plurality of values, wherein each of the second plurality of values is based, at least in part, on an inference of the first user'"'"'s expertise level with respect to a topic; and

generating a recommendation for delivery to the first user, wherein the recommendation is generated by a recommendation function executed on a processor-based device in accordance with the first plurality of values and the second plurality of values.

2. The method of claim 1, further comprising:

accessing the second plurality of values, wherein the user'"'"'s expertise level with respect to the topic is inferred from a plurality of behaviors, wherein each of the plurality of behaviors corresponds to a different usage behavior category.

3. The method of claim 1, further comprising:

accessing the second plurality of values, wherein one of the second plurality of values is generated in accordance with an expertise level calibration.

4. The method of claim 3, further comprising:

accessing the second plurality of values, wherein the one of the second plurality of values is generated in accordance with the expertise level calibration, wherein the expertise level calibration is selected from a group consisting of a test result, a certification, an attained degree, an educational curriculum, and a profession.

5. The method of claim 1, further comprising:

generating the recommendation, wherein the recommendation is generated in accordance with a comparison of the second plurality of values with a third plurality of values, wherein each of the third plurality of values are indicative of an expertise level associated with a second user.

6. The method of claim 1, further comprising:

generating the recommendation, wherein the recommendation is generated in accordance with a comparison of the second plurality of values with an assessment of a user expertise level that is appropriate for receipt of a recommendation comprising a specific object.

7. The method of claim 1, further comprising:

generating an explanation for delivery to the first user as to why the recommendation was delivered to the first user.

8. A computer-implemented system, comprising:

a first plurality of values, wherein each of the first plurality of values is based, at least in part, on an inference of a first user'"'"'s interest level with respect to a topic;

a second plurality of values, wherein each of the second plurality of values is based, at least in part, on an inference of the first user'"'"'s expertise level with respect to a topic; and

a recommendation function executed on a processor-based device that generates a recommendation for delivery to the first user, wherein the recommendation is generated in accordance with the first plurality of values and the second plurality of values.

9. The system of claim 8, further comprising:

the second plurality of values, wherein the inference of the first user'"'"'s expertise level is inferred from a plurality of behaviors, wherein each of the plurality of behaviors corresponds to a different usage behavior category.

10. The system of claim 8, further comprising:

the second plurality of values, wherein one of the second plurality of values is generated in accordance with an expertise level calibration.

11. The system of claim 10, further comprising:

the second plurality of values, wherein the one of the second plurality of values is generated in accordance with the expertise level calibration, wherein the expertise level calibration is selected from a group consisting of a test result, a certification, an attained degree, an educational curriculum, and a profession.

12. The system of claim 8, further comprising:

the recommendation function, wherein the recommendation function generates the recommendation in accordance with comparing the second plurality of values with a third plurality of values, wherein each of the third plurality of values are indicative of an expertise level associated with a second user.

13. The system of claim 12, further comprising:

a complementary expertise identification function that identifies a complementary level of expertise between the second plurality of values and the third plurality of values.

14. The system of claim 8, further comprising:

the recommendation function, wherein the recommendation function generates the recommendation in accordance with a comparison of the second plurality of values with an assessment of a user expertise level that is appropriate for the receipt of a recommendation comprising a specific object.

15. The system of claim 14, further comprising:

the user expertise level that is appropriate for the receipt of the recommendation comprising the specific object, wherein the expertise level that is appropriate is inferred from the contents of the specific object.

16. The system of claim 8, further comprising:

an explanatory function that generates an explanation as to why the recommendation was generated for delivery to the first user.

17. A computer-implemented system, comprising:

a first user'"'"'s calibrated expertise level with respect to a topic;

an expertise level value associated with a second user, wherein the expertise level value is based, at least in part, on an inference of the second user'"'"'s relative expertise level with respect to the topic and the calibrated expertise level; and

a recommendation function executed on a processor-based device that generates a recommendation for delivery to the second user, wherein the recommendation is generated in accordance with the expertise level value associated with the second user.

18. The system of claim 17, further comprising:

the first user'"'"'s calibrated expertise level with respect to the topic, wherein the calibrated expertise level is determined by an expertise calibration, wherein the expertise calibration is selected from a group consisting of a test result, a certification, an attained degree, an educational curriculum, and a profession.

19. The system of claim 17, further comprising:

the expertise level value associated with the second user, wherein the expertise level value is based, at least in part, on the inference of the second user'"'"'s relative expertise level with respect to the topic, wherein the inference is based on a plurality of behaviors, each of the plurality of behaviors corresponding to a different usage behavior category.

20. The system of claim 17, further comprising:

the recommendation function executed on a processor-based device that generates the recommendation for delivery to the second user, wherein the recommendation is generated in accordance with a comparison of the expertise level value associated with the second user and an expertise level value associated with a third user.