ILP-Based Personalized Questionnaire Generation Using Skill Graphs
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Solution Overview
Problem
Large multinational IT companies face challenges in ensuring consistent, uniform, efficient, and objective interviews for recruiting tens of thousands of employees annually, given the diversity of candidates and complexity of job requirements.
Innovation Solution
A processor-implemented method using integer linear programming (ILP) to generate a personalized optimal questionnaire based on a candidate's resume, job description, and a skill graph, ensuring optimal question selection that maximizes coverage and meets constraints such as time budget and difficulty level distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interviewing processes are used for large numbers of candidates, then human judgment and flexibility are maintained, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system enables self-service by automatically generating personalized questionnaires without requiring manual interviewer intervention. The ILP-based system autonomously processes candidate data, skill graphs, and question banks to produce optimized interview question sets, eliminating the need for human reviewers to manually craft questions for each candidate.
Solution Approach 2:
The system changes parameters by transforming unstructured candidate information into structured optimization parameters for the ILP model. Candidate resumes, skill graphs, and job requirements are converted into quantifiable parameters (weights, constraints, objective functions) that drive automatic questionnaire generation, enabling scalable processing of large candidate volumes.
2Reliability
If standardized interviewing procedures are implemented across multiple interviewers, then consistency and uniformity improve, but the complexity of managing and coordinating interviews increases
Solution Approach 1:
The system achieves universality by creating a single standardized questionnaire generation platform that serves multiple interviewers, candidates, and job roles simultaneously. The ILP-based system universally applies the same optimization principles across all interviews, ensuring consistency while handling diverse candidate profiles and job requirements through parameter adjustments rather than separate processes.
Solution Approach 2:
The system introduces an intermediary layer between candidate data and interview questions through the skill graph and ILP optimization model. This intermediary automatically translates job requirements and candidate profiles into standardized question sets, eliminating the need for direct human coordination while maintaining consistency across all interviews.
3Loss of information
If comprehensive question banks are used to cover all job requirements, then assessment coverage improves, but the time required to select and administer questions increases
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring the comprehensive question bank into skill-based categories with associated weights and difficulty levels before interview generation. The skill graph and ILP model pre-compute optimal question combinations based on job requirements, so that during actual interview generation, only parameter adjustments are needed rather than searching through the entire question bank.
Solution Approach 2:
The system segments the comprehensive question bank into skill-specific modules organized through the skill graph structure. Each skill area is independently weighted and can be optimized separately through the ILP model, allowing comprehensive coverage to be achieved through modular combination rather than selecting from a monolithic question set.
4Measurement precision
If personalized questionnaires are generated for each candidate, then assessment accuracy and relevance improve, but the computational resources and processing time required increase
Solution Approach 1:
The system achieves efficient personalization by changing parameters rather than restructuring the entire questionnaire for each candidate. The ILP model adjusts weights, constraints, and selection criteria based on candidate-specific data from resumes and skill graphs, allowing rapid generation of personalized questionnaires through parameter optimization rather than complete reconstruction.
Data Source
AI summary
The method and system of the present disclosure facilitate automatic, and in an objective manner, selection of an optimal set of technical questions, from a question bank, personalized for a candidate. This ensures consistent, standardized, efficient and objective interviews that result in high quality recruitment, given the diversity of candidates, complexity of job requirements and interviews are inherently subjective. The state-of-the-art depends on responses so far, to generate on-the-fly questions causing a cognitive load on the interviewer. Also, there is no guarantee on the breadth and depth of concepts assessed in each interview. In the present disclosure, skill graphs are employed to create a semantically rich and detailed characterization of questions in terms of concepts. Optimization formulation uses the skill graph to generate constraints, content balancing and objective functions for selection of questions.