SYSTEM AND METHOD FOR MATCHING A PROFILE TO A SPARSELY DEFINED REQUEST

• US 20140149513A1
• Filed: 11/25/2013
• Published: 05/29/2014
• Est. Priority Date: 11/23/2012
• Status: Abandoned Application

• Current Assignee: The Extraordinaries, Inc.
• Original Assignee: The Extraordinaries, Inc.
• Inventors: Rigby, Ben, Grennan, Patrick, Nelson, Mark, Snodgrass, Jordan
• Application Number:
  US14/089,196
• Publication Number:
  US 20140149513A1
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method and system that can include providing a request matching service within a community, receiving a request to match a task request with a first profile of the community, calculating a set of multi-layered composite similarity scores, selecting at least one matched task request according to composite scores, and outputting at least one matched task request of the first profile.

Claims

1. A method for matching a task request to a profile of a first community, where the profiles complete interactions of a task request through the first community comprising:

vectorizing at least one field of a task request and at least one field of an operator profile for a plurality of task requests and operator profiles of the first community;

receiving a request to match a task request with a first profile of the first community;

for each inspected task request from the plurality of task requests of the first community, calculating at least one profile vector similarity score of the first profile compared to the profiles that completed the inspected task;

for each inspected task request from the plurality of task requests of the first community, calculating at least one task vector similarity score of the inspected task compared with a set of tasks with completed interactions by the first profile;

calculating a composite score for each inspected task request from the plurality of task requests of the first community, wherein calculating the composite score of a task request comprises applying a weighting heuristic to at least one task vector similarity score and at least one profile vector similarity score;

selecting at least one matched task request according to the composite scores of the plurality of task requests; and

outputting the at least one matched task request of the first profile.

2. The method of claim 1, further comprising for each inspected task request from the plurality of task requests of the first community, calculating a direct similarity score of the inspected task compared to the first profile;

wherein calculating the composite score of a task request comprises applying a weighting heuristic to at least one task vector similarity score, at least one profile vector similarity score, and the direct similarity score.

3. The method of claim 2, wherein the profile vector similarity score is a content boosted collaborative filtering (CBCF) sub-score;

wherein the task vector similarity score is a content boosted collaborative filtering sub-score; and

wherein composite score is a weighted content boosted collaborative filtering score.

4. The method of claim 3, wherein calculating the direct similarity score comprises performing fuzzy string search of content of the profile to the inspected task request.

5. The method of claim 1, wherein the plurality of task requests includes completed task requests that have satisfied an interaction condition;

and wherein selecting at least one matched task request further comprises deprioritizing a completed task request.

6. The method of claim 5, further comprising a task request satisfying an interaction condition when a profile of the community completes a goal of the task request.

7. The method of claim 1, wherein calculating at least one profile vector similarity score comprises calculating a first profile vector similarity score of a vector of a first profile field and calculating a second profile vector similarity score of a vector of a second profile field;

and wherein calculating at least one task vector similarity score comprises calculating a first task vector similarity score of a vector of a first task field and calculating a second task vector similarity score of a vector of a second task field.

8. The method of claim 1, further comprising modifying the weighting heuristic of the first community at a first time instance from a prior time instance.

9. The method of claim 8, further comprising receiving a preference of community matching;

and further comprising modifying the weighting according to the received preference matching.

10. The method of claim 8, wherein modifying the weighting heuristic of the first community comprises when calculating the composite score of a task request, modifying weight values according to a schedule based on lifetime of the community.

11. The method of claim 10, wherein modifying weight values according to a schedule based on lifetime of the community comprises modifying weights according to a schedule based on number of interactions during lifetime of the community.

12. The method of claim 1, wherein vectorizing at least one field of a task request and at least one field of an operator profile further comprises vectorizing at least one field of a task request according to a first feature set and vectorizing at least one field of an operator profile according to a second feature set;

13. The method of claim 12, further comprising supplementing the first feature set with a third feature set of a second community;

and supplementing the second feature set with a fourth feature set of the second community.

14. The method of claim 13, further comprising selecting the second community according to type of community of the first community and type of community of the second community.

15. The method of claim 13, further comprising after the first community satisfies a mature state terminating use of the third feature set and the fourth feature set.

16. The method of claim 12, further comprising sharing the first feature set and the second feature set with a second community.

17. A method comprising:

obtaining a plurality of tasks submitted to a first community;

obtaining a plurality of profiles submitted to the first community;

vectorizing at least one field of obtained tasks and at least one field of obtained profiles;

providing an interface through which a profile can complete interactions with a task and recording a mapping between a task and a profile when profile completes an interaction with a task;

receiving a request to match at least one task to a first profile of the plurality of profiles;

generating a set of sub-scores for each inspected task of the plurality of tasks,

for at least one sub-score of the set of sub-scores calculating a profile similarity score of a first vector of the first profile to first vectors of profiles that are mapped to the inspected task;

for at least one sub-score of the set of sub-scores, calculating a task similarity score for the similarity between the inspected task to each task mapped to the first profile.

for at least one sub-score of the set of sub-scores, calculating similarity score for each inspected task compared to the first profile;

for each inspected task, calculating a composite score from the set of sub-scores of a corresponding inspected task, wherein calculating the composite score comprises applying a weighting heuristic to at least one task similarity score and at least one profile similarity score;

selecting at least one matched task request according to the composite scores of the plurality of tasks; and

outputting the at least one matched task request of the first profile.

18. The method of claim 17, wherein the profile similarity score is a content boosted collaborative filtering (CBCF) sub-score;

wherein the task similarity score is a content boosted collaborative filtering sub-score; and

wherein composite score is a weighted content boosted collaborative filtering score.

19. The method of claim 17, wherein calculating at least one profile similarity score comprises calculating a first profile similarity score of a vector of a first profile field and calculating a second profile similarity score of a vector of a second profile field;

and wherein calculating at least one task similarity score comprises calculating a first task similarity score of a vector of a first task field and calculating a second task similarity score of a vector of a second task field.

20. The method of claim 17, further comprising receiving community type configuration of the first community;

wherein vectorizing at least one field of obtained tasks and at least one field of obtained profiles comprises vectorizing at least one field of obtained tasks and at least one field of obtained profiles according to a first set of features sets; and

further comprising supplementing the first set of feature sets with a second set of feature sets of a second community.