Recommendation platform for skill development
Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.
1 . A temporary staffing application-implemented method for automatic document processing that generates a resume text file comprising:
presenting, by the temporary staffing application, users with temp work positions, each of the temp work positions including a set of predetermined skill tags based on a type of requested work that corresponds to the temp work positions,
wherein the set of predetermined skill tags is based on job templates selected in the temporary staffing application by employers associated with the temp work positions, and
wherein each of the set of skill tags indicates a skill to be employed by a given user during performance of a respective temp work position, the temporary staffing application including a userbase that accepts and staffs the temp work positions, the userbase having profiles;
tracking, by the temporary staffing application, a first temp work position completed via dispatch through the temporary staffing application by a first user,
wherein the first temp work position is one of the temp work positions presented to the first user by the temporary staffing application;
recording to a first user profile, by the temporary staffing application, each skill tag associated with the first temp work position tracked by the temporary staffing application and further recording time logged associated with each skill tag and an employer associated with the time logged,
wherein each skill tag associated with the first temp work position is based on a first job template selected in the temporary staffing application by an employer associated with the first temp work position;
executing, by a processor, an artificial machine learning model including a training data set of resumes and links between resume content and the set of predetermined skill tags;
structuring, by the temporary staffing application, a query to the artificial machine learning model associated with the first user profile that includes the set of skill tags and the time logged to each of the skill tags by corresponding employer;
determining, in response to the query, by the artificial machine learning model, a resume template to be used based at least on a count of temp work positions completed by the first user or a presence of a specialized skill in the set of predetermined skill tags,
wherein the resume template is further determined by the artificial machine learning model by identifying a path in an artificial neural network for which the generated resume text file includes one or more traits; and
automatically generating the resume text file for the first user, by the artificial machine learning model, according to the determined resume template, the resume text file including procedurally generated descriptions of experience the first user has based on the structured query.
2 . The method of claim 1 , further comprising:
determining, by the artificial machine learning model, the resume template to employ from a predetermined set based on a mix of skill tags and employers amassed by the first user and biased towards given skill tags that often occur together or employer and based on a direct relationship to a number of occurrences of the given skill tags or the employers in the first user profile.
3 . The method of claim 1 , wherein the artificial machine learning model uses as input, content of the first user profile, wherein skilled work is weighted as compared to unskilled work, as indicated by skill tags, in structuring the resume text file.
4 . The method of claim 1 , further comprising:
in response to completion of a second temp work position by the first user, updating the resume text file based on the skill tags associated with the second temp work position performed by the first user.
5 . The method of claim 1 , further comprising:
receiving user onboarding data from the first user while the first user is registering for the temporary staffing application, wherein the artificial machine learning model draws from the first user's user onboarding data to generate the resume text file.
6 . The method of claim 1 , further comprising:
receiving an indication that the first user has a first certification; and
wherein the artificial machine learning model includes a section in the resume text file associated with certifications, the section populated with at least the first certification.
7 . The method of claim 1 , wherein said generating of the resume text file is further based on reviews of the given user by a given employer associated with the first or the second temp work position.
8 . A system of automatic document processing that generates a resume text file comprising:
a processor; and
a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations including:
presenting, by a temporary staffing application, users with temp work positions, each of the temp work positions including a set of predetermined skill tags based on a type of requested work that corresponds to the temp work positions,
wherein the set of predetermined skill tags is based on job templates selected in the temporary staffing application by employers associated with the temp work positions, and
wherein each of the set of skill tags indicates a skill to be employed by a given user during performance of a respective temp work position, the temporary staffing application including a userbase that accepts and staffs the temp work positions, the userbase having profiles;
tracking, by the temporary staffing application, a first temp work position completed via dispatch through the temporary staffing application by a first user,
wherein the first temp work position is one of the temp work positions presented to the first user by the temporary staffing application;
recording to a first user profile, by the temporary staffing application, each skill tag associated with the first temp work position tracked by the temporary staffing application and further recording time logged associated with each skill tag and an employer associated with the time logged,
wherein each skill tag associated with the first temp work position is based on a first job template selected in the temporary staffing application by an employer associated with the first temp work position;
executing, by the processor, an artificial machine learning model including a training data set of resumes and links between resume content and the set of predetermined skill tags;
structuring, by the temporary staffing application, a query to the artificial machine learning model associated with the first user profile that includes the set of skill tags and the time logged to each of the skill tags by corresponding employer;
determining, in response to the query, by the artificial machine learning model, a resume template to be used based at least on a count of temp work positions completed by the first user or a presence of a specialized skill in the set of predetermined skill tags,
wherein the resume template is further determined by the artificial machine learning model by identifying a path in an artificial neural network for which the generated resume text file includes one or more traits; and
automatically generating the resume text file for the first user, by the artificial machine learning model, according to the determined resume template, the resume text file including procedurally generated descriptions of experience the first user has based on the structured query.
9 . The system of claim 8 , wherein the artificial machine learning model uses as input, content of the first user profile, wherein skilled work is weighted as compared to unskilled work, as indicated by skill tags, in structuring the resume text file.
10 . The system of claim 8 , wherein the performed operations further include:
determining, by the artificial machine learning model, the resume template to employ from a predetermined set based on a mix of skill tags and employers amassed by the first user and biased towards given skill tags that often occur together or employer and based on a direct relationship to a number of occurrences of the given skill tags or the employers in the first user profile.
11 . The system of claim 8 , wherein the performed operations further include:
in response to completion of a second temp work position by the first user, updating the resume text file based on the skill tags associated with the second temp work position performed by the first user.
12 . The system of claim 8 , wherein the performed operations further include:
receiving user onboarding data from the user while the user is registering for the temporary staffing application, wherein the artificial machine learning model draws from the user onboarding data to design the resume text file.
13 . The system of claim 8 , wherein the performed operations further include:
receiving an indication that the first user has a first certification; and
wherein the artificial machine learning model includes a section in the resume text file associated with certifications, the section populated with at least the first certification.
14 . The system of claim 8 , wherein said generating of the resume text file is further based on reviews of the given user by a given employer associated with the first or the second temp work position.
15 . A non-transitory computer-readable medium of automatic document processing that generates a resume text file, the computer-readable medium contains a plurality of instructions that when executed by a processor cause the processor to perform operations including:
presenting, by a temporary staffing application, users with temp work positions, each of the temp work positions including a set of predetermined skill tags based on a type of requested work that corresponds to the temp work positions,
wherein the set of predetermined skill tags is based on job templates selected in the temporary staffing application by employers associated with the temp work positions, and
wherein each of the set of skill tags indicates a skill to be employed by a given user during performance of a respective temp work position, the temporary staffing application including a userbase that accepts and staffs the temp work positions, the userbase having profiles;
tracking, by the temporary staffing application, a first temp work position completed via dispatch through the temporary staffing application by a first user,
wherein the first temp work position is one of the temp work positions presented to the first user by the temporary staffing application;
recording to a first user profile, by the temporary staffing application, each skill tag associated with the first temp work position tracked by the temporary staffing application and further recording time logged associated with each skill tag and an employer associated with the time logged,
wherein each skill tag associated with the first temp work position is based on a first job template selected in the temporary staffing application by an employer associated with the first temp work position;
executing, by the processor, an artificial machine learning model including a training data set of resumes and links between resume content and the set of predetermined skill tags;
structuring, by the temporary staffing application, a query to the artificial machine learning model associated with the first user profile that includes the set of skill tags and the time logged to each of the skill tags by corresponding employer;
determining, in response to the query, by the artificial machine learning model, a resume template to be used based at least on a count of temp work positions completed by the first user or a presence of a specialized skill in the set of predetermined skill tags,
wherein the resume template is further determined by the artificial machine learning model by identifying a path in an artificial neural network for which the generated resume text file includes one or more traits; and
automatically generating the resume text file for the first user, by the artificial machine learning model, according to the determined resume template, the resume text file including procedurally generated descriptions of experience the first user has based on the structured query.
16 . The non-transitory computer-readable medium of claim 15 , wherein the performed operations further include:
determining, by the artificial machine learning model, the resume template to employ from a predetermined set based on a mix of skill tags and employers amassed by the first user and biased towards given skill tags that often occur together or employer and based on a direct relationship to a number of occurrences of the given skill tags or the employers in the first user profile.
17 . The non-transitory computer-readable medium of claim 15 , wherein the performed operations further include:
in response to completion of a second temp work position by the first user, updating the resume text file based on the skill tags associated with the second temp work position performed by the first user.
18 . The non-transitory computer-readable medium of claim 15 , wherein the performed operations further include:
receiving user onboarding data from the user while the user is registering for the temporary staffing application, wherein the artificial machine learning model draws from the user onboarding data to design the resume text file.
19 . The non-transitory computer-readable medium of claim 15 , wherein the performed operations further include:
receiving an indication that the first user has a first certification; and
wherein the artificial machine learning model includes a section in the resume text file associated with certifications, the section populated with at least the first certification.
20 . The non-transitory computer-readable medium of claim 15 , wherein said generating of the resume text file is further based on reviews of the given user by a given employer associated with the first or the second temp work position.