Clustering User Questions by Similarity Features
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Solution Overview
Problem
Current Question and Answer (QA) systems, such as IBM Watson, do not effectively identify and categorize similar lines of questioning across different users, which can lead to missed opportunities for collaboration, targeted advertising, or detection of potential illegal activities, as they primarily focus on generating answers rather than analyzing user queries and evidence for similarity.
Innovation Solution
A method and system that cluster Question-Answer-Evidence Passage (Q-A-P) triplets based on extracted features, allowing for the identification of similar lines of questioning across users, enabling collaboration, communication, or targeted advertising by grouping users with similar interests or topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If QA systems focus on generating answers from input questions, then answer accuracy is improved, but user similarity identification capability deteriorates
Solution Approach 1:
The system performs multiple functions: it generates answers to questions while simultaneously clustering users based on their questions and answers. The QA system is extended to include user profiling and similarity detection capabilities, allowing it to serve both answer generation and user categorization purposes.
Solution Approach 2:
The system segments user interactions into discrete question-answer pairs that can be individually analyzed and clustered. By breaking down user behavior into specific Q-A units with extracted features, the system can process and categorize users based on patterns in their questioning and answering behaviors.
2Measurement precision
If the system analyzes and clusters user questions and answers, then user categorization accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts features from questions and answers in advance, creating structured user profiles before clustering occurs. This preliminary feature extraction organizes raw data into manageable components, simplifying the subsequent clustering process and reducing computational complexity.
Solution Approach 2:
The system introduces intermediate representations (extracted features, user profiles, and cluster assignments) that mediate between raw user interactions and final user categorization. These intermediaries simplify the complexity by providing structured layers between input data and output classifications.
3Adaptability or versatility
If the system clusters users based on similar lines of questioning, then collaboration opportunities are improved, but information processing time increases
Solution Approach 1:
The system performs partial clustering by focusing on key extracted features from questions and answers rather than analyzing all possible aspects of user interactions. This selective approach enables collaboration identification without requiring complete analysis of all user data, reducing processing time while maintaining effectiveness.
Data Source
AI summary
Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.