Personalized Questionnaire System Using Graph Clustering
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Solution Overview
Problem
Traditional questionnaires are inefficient and inaccurate due to their reliance on default questions, leading to user disinterest, irrelevant questions, and potential misclassification, as they fail to tailor the questioning process to individual users' specific needs or situations.
Innovation Solution
A computer-implemented method that generates personalized questions by analyzing user responses to an initial set of questions, forming clusters based on correlations, and selecting subsequent questions that are more relevant to the user's profile, thereby improving user compliance and diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional default questions are used for all users, then the questionnaire structure is simple and uniform, but the relevance to individual users is low and user disinterest occurs
Solution Approach 1:
The system pre-computes and stores the question graph structure, question clusters, and relevance relationships before actual questionnaire administration. This preliminary structuring of all possible questions and their interrelationships enables rapid personalized questionnaire generation without complex real-time computation, resolving the contradiction between personalization and system complexity
Solution Approach 2:
The questionnaire system segments the complete set of questions into distinct clusters based on topics and relationships. Each cluster can be independently selected and combined to form personalized questionnaires, allowing the system to provide adaptability without requiring a completely new questionnaire structure for each user
2Measurement precision
If more questions are asked to improve categorization accuracy, then the diagnostic precision improves, but the user fatigue and time consumption increase
Solution Approach 1:
The system asks only the necessary subset of questions from each relevant cluster rather than all possible questions. By selecting a partial set of high-value questions based on the user's initial responses and the graph structure, the system achieves sufficient categorization accuracy without requiring excessive questions that would cause user fatigue
Solution Approach 2:
The questionnaire adapts dynamically by adjusting the number and type of questions asked based on the user's initial responses. The system modifies the questionnaire structure in real-time, asking follow-up questions only where needed to resolve uncertainty in categorization, thereby balancing accuracy with time efficiency
3Adaptability or versatility
If more questions are asked to cover all potential categories, then the comprehensiveness improves, but the user compliance and data quality decrease
Solution Approach 1:
The system applies different questioning strategies to different user contexts and categories. Rather than uniformly asking the same comprehensive set of questions from all users, it tailors the question selection to each user's specific situation, initial responses, and likely category memberships, making the questionnaire easier to complete while maintaining comprehensive category coverage
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of answers to a first set of questions. The actions include generating an adjacency matrix based on the question-answer pairs. The actions include determining a network graph that includes question nodes and edges. The actions include identifying one or more clusters of question nodes by applying a community detection algorithm on the network graph. The actions include determining, for each cluster, i) a cluster centrality and ii) a cluster magnitude. The actions include ranking the clusters based on the cluster centralities and the cluster magnitudes of the one or more clusters. The actions include selecting a second set of questions for the user. And, the actions include causing the questions from the second set of questions to be presented to the user.