Semantic Matching via Continuous User Feedback
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Solution Overview
Problem
Existing systems face challenges in automatically identifying semantically similar service tickets or incident reports due to differences in description terms, syntactic structure, and incomplete information, which hinders effective worker assignment based on experience and performance.
Innovation Solution
A device and method that identify semantically similar textual content samples by using natural language processing to determine similarity scores and acceptance information, iteratively updating a model based on user input to improve the accuracy of relevant sample identification and worker allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text matching methods are used to identify similar service tickets, then the system is simple to implement, but the accuracy of identifying semantically similar samples is low due to differences in description terms and syntactic structure
Solution Approach 1:
The system implements continuous feedback loops where user interactions (views, resolutions, ratings) with identified similar samples are fed back to retrain and refine the semantic matching model. This feedback mechanism progressively improves measurement precision by learning from actual user behavior patterns while maintaining automated operation, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The semantic matching system performs self-improvement through automated retraining using accumulated user feedback data without requiring manual intervention. The model automatically adjusts its parameters and weighting based on observed user interactions, enabling the system to enhance its own accuracy while keeping the interface simple for end users.
2Measurement precision
If comprehensive semantic analysis is performed on all samples to improve matching accuracy, then identification precision improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial semantic analysis by focusing computational resources on the most discriminative features and attributes of samples rather than analyzing all possible characteristics equally. It identifies and prioritizes key semantic elements that contribute most to matching accuracy, reducing overall computational burden while maintaining high precision through targeted analysis of critical features.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as similarity thresholds, feature weighting, and matching criteria based on the specific context and sample characteristics. By changing parameters adaptively rather than using fixed comprehensive analysis, the system optimizes the balance between precision and computational resource consumption for different query scenarios.
3Loss of information
If the system processes and presents all potentially relevant samples to users, then completeness of information is improved, but the time for users to find relevant information increases
Solution Approach 1:
The system segments the set of potentially relevant samples into hierarchical groups based on similarity metrics and user interaction patterns. Instead of presenting all samples in a single list, it divides them into categories or ranked groups that guide users to the most relevant information first, maintaining completeness while reducing search time through structured organization.
Solution Approach 2:
The system performs preliminary ranking and filtering of samples based on semantic similarity and historical user interaction data before presenting them to users. By pre-processing and ordering samples according to predicted relevance, the system ensures that the most useful information appears first, allowing users to find relevant samples quickly while still providing access to the complete set if needed.
Data Source
AI summary
A method may include receiving, by a device, an input sample of textual content. The method may include identifying, by the device, a comparison sample that is semantically similar to the input sample. The comparison sample may be identified based on a similarity score, of the comparison sample and the input sample, satisfying a semantic similarity threshold. The method may include identifying, by the device, a plurality of output samples of textual content based on acceptance information corresponding to the plurality of output samples and the comparison sample. The acceptance information may be determined based on a user input regarding similarity or relevance of the plurality of output samples and the comparison sample, and the user input may be received before the input sample is received.