Job Skill Inference via Applicant Profile Analysis
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Solution Overview
Problem
Current social networking systems lack an effective skills inference model to automatically determine the skills required for job postings, as most job postings do not include a desired skills section, and existing models primarily focus on inferring member skills rather than job skills, leading to incomplete job matching and reduced click-through rates in job searches.
Innovation Solution
A skill determination system that infers job skills using member data from job applicants, calculating a confidence score based on affinity scores, skill frequency, and other factors to match members with relevant job postings, thereby improving job matching and click-through rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If job postings do not include a desired skills section, then the job posting structure remains simple and easy to create, but the skill information is missing and cannot be used for accurate job matching
Solution Approach 1:
The system performs preliminary action by inferring skills before the job posting is fully processed. The skill inference model analyzes job titles, descriptions, and applicant data to pre-determine skill requirements, which are then associated with the job posting without requiring explicit input from the employer.
Solution Approach 2:
The system uses self-service by automatically generating skill information from existing job posting data and applicant profiles. The skill inference model processes the job title and description to self-determine required skills, eliminating the need for manual skill entry while maintaining information accuracy.
2Device complexity
If existing skill inference models focus on inferring member skills rather than job skills, then the model structure remains simple, but job matching accuracy is reduced and click-through rates decrease
Solution Approach 1:
The skill inference model achieves universality by serving multiple functions: it infers both member skills from profiles and job skills from postings, and simultaneously improves job matching accuracy. This multi-functional approach eliminates the need for separate models while enhancing overall system performance.
Solution Approach 2:
The system applies parameter changes by transforming the inference approach from member-centric to job-centric. The model changes its input parameters to focus on job title, description, and applicant data, thereby inferring job skills rather than member skills, which directly improves matching accuracy.
3Adaptability or versatility
If the system presents all job postings to members, then the job search coverage is comprehensive, but the time required for members to review postings increases and resource efficiency decreases
Solution Approach 1:
The system implements local quality by tailoring job posting recommendations to individual member skill profiles. Instead of presenting all postings uniformly, the system locally adapts the job list based on each member's inferred skills, ensuring high relevance while reducing review time through personalized filtering.
Data Source
AI summary
Techniques for inferring a specific skill associated with a job posting are described. In an example, disclosed is a system that selects, from a jobs database, a specific job posting from a plurality of job postings. Additionally, job applicants for the specific job posting can be determined using indicators in the profile data of members. Moreover, a set of skills associated with the job applicants can be obtained. Furthermore, a percentage of the job applicants having a specific skill from the set of skills can be determined using the profile data of the job applicants. Subsequently, a confidence score of the specific skill being associated with the specific job posting can be calculated based on the percentage of the job applicants having the specific skill. A user interface can display a presentation of the specific job posting to a first member when the confidence score transgresses a predetermined score.