Job Search Grouping with Affinity Scoring
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Solution Overview
Problem
Current job search methods on social networks are limited as they focus solely on job title matches and job descriptions, missing valuable opportunities by not considering member preferences and additional relevant information that could lead to better job postings.
Innovation Solution
The system groups job postings based on shared features and characteristics, using machine-learning algorithms to assign affinity scores that match jobs with members, presenting jobs within groups that share common attributes, and ranking these groups and jobs for personalized display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the job search engine uses strict title matching to ensure precision, then measurement precision is improved, but the quantity of relevant jobs found decreases
Solution Approach 1:
The system changes the matching parameters from strict title equality to affinity scoring based on multiple features including title similarity, description keywords, skills, and member preferences. This allows the system to identify relevant jobs that wouldn't match under strict title matching while maintaining high precision through the comprehensive scoring mechanism.
Solution Approach 2:
The patent adds multiple new dimensions to the job matching process beyond title matching, including job description analysis, skill requirements, member preferences, and affinity scores. This multi-dimensional approach expands the search space to find jobs that are relevant but wouldn't be captured by title matching alone.
2Device complexity
If the system considers only job descriptions and member profiles, then device complexity is reduced, but the quality of job recommendations deteriorates
Solution Approach 1:
The system implements a multi-functional matching mechanism that simultaneously evaluates title similarity, description keywords, skills, preferences, and affinity scores. This universal approach integrates multiple data sources and evaluation criteria into a single comprehensive recommendation system, improving quality without requiring separate systems for each factor.
Solution Approach 2:
The affinity score acts as an intermediary that synthesizes information from multiple sources including job descriptions, member profiles, skills, and preferences. This intermediary metric translates complex multi-factor evaluation into a single comparable value that drives recommendation quality while managing system complexity.
3Quantity of substance
If the system presents all candidate jobs without grouping, then the quantity of jobs presented increases, but the ease of operation for members decreases
Solution Approach 1:
The system segments the list of candidate jobs into groups based on shared characteristics such as industry, company size, location, or job type. This segmentation allows members to navigate large numbers of jobs more easily by browsing organized categories rather than a flat list, improving ease of operation while maintaining access to all relevant opportunities.
Data Source
AI summary
Methods, systems, and computer programs are presented for grouping job postings for presentation to a user in response to a search. A method includes determining the closest-matching groups of jobs for a user and presenting a display such that the closest-matching jobs are viewable within the groups. For each group, a server determines a group affinity based on a group characteristic and a user characteristic and affinities of jobs for that group based on the job postings and the group characteristic. The server ranks the groups for the user based on the group affinity score for each group, and ranks the job postings within each group based on the jobs affinity to the user. Some of the groups and job postings are presented to the user based on the ranking.