Apparatus and method for applicant scoring

1. An apparatus for applicant scoring, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

receive a video resume including a user-associated data set to be stored on a resource data storage system, wherein audiovisual speech recognition is utilized to decode the video resume to the user-associated data, and

wherein utilizing the audiovisual speech recognition further comprises:

generating an image vector as a function of the video resume;

generating an audio vector as a function of the video resume; and

concatenating the image vector and audio vector to predict the user-associated data;

parse the user-associated data set into a keyword record;

generate a user metric, as a function of the keyword record, based on a plurality of weighted values reflecting desirability, wherein generating the user metric comprises:

identifying at least a negative factor in the keyword record; and

adjusting the plurality of weighted values as a function of the at least a negative factor;

receive a plurality of postings, wherein receiving the plurality of postings comprises:

selecting the plurality of postings as a function of relevant information, wherein relevant information comprises a required user metric of the plurality of postings; and

compiling the plurality of job postings on an immutable sequential listing; and

match a posting of the plurality of postings to a user as a function of the user metric, wherein matching the posting to the user comprises:

training a machine learning model using training data and a classification algorithm, wherein the training data correlates previous user metric data and posting data; and

identifying the posting using the trained machine learning model, wherein the user metric is provided to the trained machine learning model as an input to output the posting.

2. The apparatus of claim 1 , wherein parsing the user-associated data comprises using a machine learning module to generate the keyword record, wherein the machine learning module comprises a keyword classifier, wherein the keyword classifier is trained using keyword classifier training data comprising a plurality of previous user-associated data sets.

3. The apparatus of claim 2 , wherein the keyword classifier comprises a neural network.

4. The apparatus of claim 1 , wherein the user-associated data set comprises visual content.

5. The apparatus of claim 1 , further configured to parse the keyword record using a language processing module.

6. The apparatus of claim 1 , wherein the plurality of weighted values is further adjusted as a function of a positive factor.

7. The apparatus of claim 1 , wherein the user-associated data set comprises a verification.

8. The apparatus of claim 7 , wherein the processor is further configured to verify the user associated data set, wherein verifying the user associated data set comprises transmitting the user-associated data set to a validator and receiving a verification.

9. The apparatus of claim 7 , wherein the verification is digitally signed.

10. The apparatus of claim 1 , further configured to store the user metric on an immutable sequential listing.

11. A method for applicant scoring, the method comprising:

receiving a video resume including a user-associated data set to be stored on a resource data storage system, wherein audiovisual speech recognition is utilized to decode the video resume to the user-associated data, and wherein utilizing the audiovisual speech recognition further comprises:

generating an image vector as a function of the video resume;

generating an audio vector as a function of the video resume; and

concatenating the image vector and audio vector to predict the user-associated data;

parsing the user-associated data set into a keyword record;

generating a user metric as a function of the keyword record, based on a plurality of weighted values reflecting desirability, wherein generating the user metric comprises:

identifying at least a negative factor in the keyword record; and

adjusting the plurality of weighted values as a function of the at least a negative factor;

receiving a plurality of postings, wherein receiving the plurality of postings comprises:

selecting the plurality of postings as a function of relevant information, wherein relevant information comprises a required user metric of the plurality of postings; and

compiling the plurality of job postings on an immutable sequential listing; and

matching a posting of the plurality of postings to a user as a function of the user metric, wherein matching the posting to the user comprises:

training a machine learning model using training data and a classification algorithm, wherein the training data correlates previous user metric data and posting data; and

identifying the posting using the trained machine learning model, wherein the user metric is provided to the trained machine learning model as an input to output the posting.

12. The method of claim 11 , wherein parsing the user-associated data comprises using a machine learning module to generate the keyword record, wherein the machine learning module comprises a keyword classifier, wherein the keyword classifier is trained using keyword classifier training data comprising a plurality of previous user-associated data sets.

13. The method of claim 12 , wherein the keyword classifier comprises a neural network.

14. The method of claim 11 , wherein the user-associated data set comprises visual content.

15. The method of claim 11 , further comprising generating the keyword record using a language processing module operating on at least a processor.

16. The method of claim 11 , wherein the plurality of weighted values is further adjusted as a function of a positive factor.

17. The method of claim 11 , wherein the user-associated data set comprises a verification.

18. The method of claim 17 , further comprising verifying the user associated data set, wherein verifying the user associated data set comprises transmitting the user-associated data set to a validator and receiving a verification.

19. The method of claim 17 , wherein the verification is digitally signed.

20. The method of claim 11 , further comprising storing the user metric on an immutable sequential listing.