Adaptive recruitment system using artificial intelligence
Methods and systems are provided for adaptive recruitment computer system. In one novel aspect, the adaptive recruitment computer system generates a question bank based on a job description, selects adaptively questions from the question bank for an interview, and generates a feedback report for the candidate based on the evaluation of the candidate's answer. In one embodiment, the computer system categorizes a job requirement based on a body of knowledge (BOK) skill knowledge base, generates a question bank, selects adaptively a subset of questions from the generated question bank, wherein each question selected is based on evaluations of candidate's answers to corresponding prior questions using a recurrent neural network (RNN) model, and generates a feedback report for the candidate, wherein the feedback report using the RNN model based on evaluations of answers and a BOK candidate knowledge base, wherein the BOK candidate knowledge base receives updates from the computer system.
1. A method, comprising:
categorizing, by a computer system with one or more processors coupled with at least one memory unit, a job requirement into a set of job skills based on a body of knowledge (BOK) skill knowledge base;
generating a question bank comprising a list of questions based on the set of job skills and a BOK question knowledge base;
selecting adaptively a subset of questions from the generated question bank for an online interview with a candidate based on a predefined rule, wherein each question selected is based on evaluations of one or more answers from the candidate to corresponding prior questions using a recurrent neural network (RNN) model; and
generating a feedback report for the candidate, wherein the feedback report using the RNN model based on evaluations of answers from the candidate and a BOK candidate knowledge base, wherein the BOK candidate knowledge base receives updates from the computer system.
2. The method of claim 1 , wherein each job skill has a set of attributes comprising a multi-level industry taxonomy, a skill level, and cross disciplinary references.
3. The method of claim 2 , wherein each question in the BOK question knowledge base has a skill level attribute, and wherein the generated question bank includes questions of different skill levels based on the skill level of the job skill attributes.
4. The method of claim 1 , wherein with recruitment Big Data, a convolution neural network (CNN) is implemented to create and update one or more BOK knowledge bases comprising the BOK skill knowledge base, the BOK question knowledge base, and the BOK candidate knowledge base.
5. The method of claim 1 , further comprising:
obtaining candidate information prior to the interview; and
generating a candidate profile and authentication information.
6. The method of claim 5 , wherein the candidate profile is generated from the candidate information using the RNN model based on the BOK candidate knowledge base.
7. The method of claim 5 , wherein the authentication information is a voice verification.
8. The method of claim 7 , wherein an original voice sample for the voice verification is obtained by extracting audio clips from an initial voice interview of the candidate.
9. The method of claim 1 , wherein the feedback report includes a deficiency report, and wherein a training recommendation list derived from the deficiency report is included.
10. The method of claim 1 , wherein the feedback report includes a strength report, and wherein a matching opening recommendation list derived from the strength report is included.
11. An apparatus comprising:
a network interface that connects the apparatus to a communication network;
a memory; and
one or more processors coupled to one or more memory units, the one or more processors configured to
categorize a job requirement into a set of job skills based on a body of knowledge (BOK) skill knowledge base;
generate a question bank comprising a list of questions based on the set of job skills and a BOK question knowledge base;
select adaptively a subset of questions from the generated question bank for an online interview with a candidate based on a predefined rule, wherein each question selected is based on evaluations of one or more answers from the candidate to corresponding prior questions using a recurrent neural network (RNN) model; and
generate a feedback report for the candidate, wherein the feedback report using the RNN model based on evaluations of answers from the candidate and a BOK candidate knowledge base, wherein the BOK candidate knowledge base receives updates from the computer system.
12. The apparatus of claim 11 , wherein each job skill has a set of attributes comprising a multi-level industry taxonomy, a skill level, and cross disciplinary references.
13. The apparatus of claim 12 , wherein each question in the BOK question knowledge base has a skill level attribute, and wherein the generated question bank includes questions of different skill levels based on the skill level of the job skill attributes.
14. The apparatus of claim 11 , wherein a CNN with input from recruitment Big Data is implemented to create and update one or more BOK knowledge bases comprising the BOK skill knowledge base, the BOK question knowledge base, and the BOK candidate knowledge base.
15. The apparatus of claim 11 , wherein the processor is further configured to: obtain candidate information prior to the interview; and generate a candidate profile and authentication information.
16. The apparatus of claim 15 , wherein the candidate profile is generated from the candidate information using the RNN model based on the BOK candidate knowledge base.
17. The apparatus of claim 15 , wherein the authentication information is a voice verification.
18. The apparatus of claim 17 , wherein an original voice sample for the voice verification is obtained by extracting audio clips from an initial voice interview of the candidate.
19. The apparatus of claim 11 , wherein the feedback report includes a deficiency report, and wherein a training recommendation list derived from the deficiency report is included.
20. The apparatus of claim 11 , wherein the feedback report includes a strength report, and wherein a matching opening recommendation list derived from the strength report is included.