Computing system with crowd-source mechanism and method of operation thereof

1. A method of operation of a computing system comprising:

receiving a request for representing the request from a user;

generating a request profile based on the request for processing the request;

determining a matching expert with a control unit for the request based on the request profile and based on regional expertise of member profiles for representing members, wherein the regional expertise is determined based on details regarding a visit by the members to a geographic area associated with the request, including a length of time spent at the geographic area; and

communicating the request for the matching expert.

2. The method as claimed in claim 1 further comprising:

determining the member profiles each including subject expertise, the regional expertise, or a combination thereof for representing each of the members;

wherein:

generating the request profile includes generating the request profile including a subject matter, a regional-association, or a combination thereof for describing the request; and

determining the matching expert includes determining the matching expert based on the subject expertise, the regional expertise, or a combination thereof corresponding to the subject matter, the regional-association, or a combination thereof.

3. The method as claimed in claim 1 further comprising:

determining a user preference for representing the user asking the request;

determining the member profiles each including a member preference for representing each of the members; and

wherein:

determining the matching expert includes determining the matching expert based on matching the member preference with the user preference.

4. The method as claimed in claim 1 further comprising:

determining a standardized rating; and

determining the member profiles each including a member preference for representing each of the members;

wherein:

determining the matching expert includes determining the matching expert based on comparing the standardized rating with the member preference.

5. The method as claimed in claim 1 wherein determining the matching expert includes determining the matching expert based on location history, previous response, response category, previous delay, requester feedback, community feedback, or a combination thereof.

6. The method as claimed in claim 1 further comprising communicating a matching response associated with the matching expert in response to the request.

7. The method as claimed in claim 6 further comprising generating the matching response based on the instance of the member profiles corresponding to the matching expert for estimating the matching response likely to be provided by the matching expert.

8. The method as claimed in claim 6 wherein:

generating the request profile includes calculating time sensitivity for the request;

communicating the request includes updating a request repository with the request based on the time sensitivity.

9. The method as claimed in claim 6 wherein:

communicating the request includes communicating the request for requesting the members to answer the request; and

communicating the matching response includes generating a response set including a ranking sequence for arranging instances of the matching response from the members based on subject expertise, the regional expertise, or a combination thereof the members.

10. The method as claimed in claim 6 further comprising communicating a requester feedback in response to the matching response.

11. A computing system comprising:

a communication unit configured to communicate a request for representing the request from a user or for a matching expert;

a control unit, coupled to the communication unit, configured to:

generate a request profile based on the request for processing the request, and

determining the matching expert for the request based on the request profile and based on regional expertise of member profiles for representing members, for communicating the request to the matching expert, wherein the regional expertise is determined based on details regarding a visit by the members to a geographic area associated with the request, including a length of time spent at the geographic area.

12. The system as claimed in claim 11 wherein the control unit is configured to:

determine the member profiles each including subject expertise, the regional expertise, or a combination thereof for representing each of the members;

generate the request profile including a subject matter, a regional-association, or a combination thereof for describing the request; and

determine the matching expert based on the subject expertise, the regional expertise, or a combination thereof corresponding to the subject matter, the regional-association, or a combination thereof.

13. The system as claimed in claim 11 wherein the control unit is configured to:

determine a user preference for representing the user asking the request;

determine the member profiles each including a member preference for representing each of the members; and

determine the matching expert based on matching the member preference with the user preference.

14. The system as claimed in claim 11 wherein the control unit is configured to:

determine a standardized rating;

determine the member profiles each including a member preference for representing each of the members; and

determine the matching expert based on comparing a standardized rating with the member preference.

15. The system as claimed in claim 11 wherein the control unit is configured to determine the matching expert based on location history, previous response, response category, previous delay, requester feedback, community feedback, or a combination thereof.

16. A non-transitory computer readable medium including instructions for a computing system comprising:

receiving a request for representing the request from a user;

generating a request profile based on the request for processing the request;

determining a matching expert for the request based on the request profile and based on regional expertise of member profiles for representing members, wherein the regional expertise is determined based on details regarding a visit by the members to a geographic area associated with the request, including a length of time spent at the geographic area; and

communicating the request for the matching expert.

17. The non-transitory computer readable medium as claimed in claim 16 wherein:

determining the member profiles includes determining the member profiles each including subject expertise, the regional expertise, or a combination thereof for representing each of the members;

generating the request profile includes generating the request profile including a subject matter, a regional-association, or a combination thereof for describing the request; and

determining the matching expert includes determining the matching expert based on the subject expertise, the regional expertise, or a combination thereof corresponding to the subject matter, the regional-association, or a combination thereof.

18. The non-transitory computer readable medium as claimed in claim 16 further comprising:

determining a user preference for representing the user asking the request;

wherein:

determining the member profiles includes determining the member profiles each including a member preference for representing each of the members; and

determining the matching expert includes determining the matching expert based on matching the member preference with the user preference.

19. The non-transitory computer readable medium as claimed in claim 16 further comprising:

determining a standardized rating;

wherein:

determining the member profiles includes determining the member profiles each including a member preference for representing each of the members; and

determining the matching expert includes determining the matching expert based on comparing a standardized rating with the member preference.

20. The non-transitory computer readable medium as claimed in claim 16 wherein determining the matching expert includes determining the matching expert based on location history, previous response, response category, previous delay, requester feedback, community feedback, or a combination thereof.