Adaptive Weighting for Document Similarity Comparison
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Solution Overview
Problem
Existing document-similarity calculation methods fail to effectively compare documents across different methodologies, neglecting the integration of various similarity metrics such as text-based, visual-based, and social network-based approaches, and do not account for user feedback or document-specific characteristics.
Innovation Solution
A system that combines document-similarity values from multiple methods using adaptive weighting, where weights are initialized and updated based on document types, structures, and user feedback, employing a weight-combination function to generate a unified similarity score, incorporating machine learning for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple document-similarity calculation methods are used to compare documents, then the comprehensiveness of similarity assessment is improved, but the complexity of the system increases
Solution Approach 1:
The patent combines multiple document-similarity calculation methods (text-based, visual-based, usage-based, and social network-based) into a unified system that produces a single comprehensive similarity score. This merging approach allows the system to leverage the strengths of each individual method while presenting a unified interface to users, thus improving measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The system creates a universal document-comparing application that can handle multiple types of documents (word processing documents, spreadsheets, presentations, images, audio, video) using a single multi-functional framework. This universal approach allows the same system architecture to serve multiple document types and similarity calculation methods, reducing the overall complexity compared to implementing separate systems for each method.
2Measurement precision
If adaptive weighting with user feedback is implemented, then the accuracy of similarity comparison is improved, but the time required for weight initialization and updating increases
Solution Approach 1:
The system performs preliminary actions by initializing weights for different document-similarity calculation methods before actual comparison operations. This preliminary weighting setup allows the system to have ready-to-use confidence levels for each method, reducing the time required during actual comparisons. The weights can be pre-configured based on document types, locations, structures, or usage patterns, so that when comparisons are needed, the system can quickly apply established weights rather than calculating them from scratch.
Solution Approach 2:
The system implements feedback mechanisms where user interactions (such as marking documents as similar or dissimilar) are used to update the weights of different calculation methods over time. This feedback loop allows the system to learn from actual usage patterns and improve its weighting strategy, gradually reducing the time needed for weight adjustments as the system becomes more familiar with specific document types and user preferences.
3Measurement precision
If document-specific characteristics are considered in weighting, then the relevance of similarity results is improved, but the computational overhead increases
Solution Approach 1:
The system applies local quality by assigning different weights to different calculation methods based on specific document characteristics. Instead of using a uniform weighting approach for all documents, the system analyzes document-type-specific factors (such as whether a document is an email, report, or presentation) and adjusts the weighting strategy accordingly. This allows the system to focus computational resources on the most relevant calculation methods for each document type, improving relevance while managing computational overhead.
Data Source
AI summary
One embodiment provides a system for comparing documents based on different document-similarity calculation methods using adaptive weighting. During operation, the system receives at least two document-similarity values associated with two documents, wherein the document-similarity values are calculated by different document-similarity calculation methods. The system then determines the weight of a respective document-similarity calculation method for each of the two documents, as well as a weight-combination function for calculating a combined weight of the respective document-similarity calculation method associated with the two documents. Next, the system generates a combined similarity value based on the document-similarity values and the weight-combination function.