Multi-Directional Attribute Matching System With Credibility Weighting
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Solution Overview
Problem
Current systems fail to effectively match items with needs by considering the varying importance and credibility of attributes across multiple sides in complex scenarios, leading to inefficient matching and dissatisfaction in domains like employment, services, and product offerings.
Innovation Solution
A multi-directional attribute matching system (MAMS) that employs a computer architecture to compute a single numerical score by matching attributes possessed by items with those needed, taking into account the hierarchy, credibility, and importance of attributes across multiple sides, using a database and processing server to aggregate ratings and generate a final match score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single numerical score is computed to match items with needs, then matching efficiency is improved, but the complexity of computing and aggregating multiple attributes with varying credibility and importance increases
Solution Approach 1:
The system segments the matching process into distinct modules: attribute extraction from multiple sources, credibility assessment of each attribute, importance weighting of different attributes, and final score aggregation. This modular segmentation allows efficient computation while managing complexity through organized processing stages.
Solution Approach 2:
The patent introduces intermediary components including a credibility assessment module that evaluates the reliability of each attribute source, and an importance weighting mechanism that mediates between multiple attributes. These intermediaries transform raw attributes into weighted scores that can be efficiently aggregated into a final match score.
2Measurement precision
If multiple attributes with varying credibility levels are considered in matching, then matching accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system applies local quality by assigning different credibility levels to different attribute sources based on their reliability. Each attribute is evaluated individually with its own credibility weight, allowing the system to give more importance to reliable sources (e.g., verified profiles, official data) and less to unreliable sources (e.g., self-reported information, unverified data).
Solution Approach 2:
The patent changes parameters by introducing credibility scores and importance weights as multiplicative factors in the matching calculation. The final match score is computed by combining attributes with varying credibility levels and importance weights, transforming the matching process from a simple comparison to a weighted aggregation that accounts for data quality and relevance.
3Reliability
If comprehensive attribute matching is performed across multiple sides, then matching completeness is improved, but the time required for computation increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing credibility assessments for each attribute source, and pre-determining importance weights for different attributes. This preliminary processing allows the actual matching computation to proceed more quickly by using pre-established parameters rather than calculating everything from scratch for each match.
Solution Approach 2:
The patent implements continuous useful action through an iterative scoring mechanism that processes attributes in sequence rather than requiring complete data before starting computation. The system can begin calculating match scores as attributes become available, continuously refining the score as more attribute information is processed, thereby reducing overall computation time while maintaining completeness.
Data Source
AI summary
A method and a multi-directional attribute matching system (MAMS) determining a degree of match between item profiles with respective attributes possessed (AP) and respective attributes needed (AN) of varying credibility and varying importance are provided. The MAMS receives an attributes possessed list including the AP and an attributes needed list including the AN, from a predefined attribute list database. The AP and the AN have parent attributes or hierarchical sub-attributes. The MAMS merges multiple occurrences of AP and inputs to the attributes possessed list. The MAMS generates a matched attribute list for a side by matching the AP in an item profile with the AN in an item profile of another item by matching the AP with the AN. The MAMS computes a raw score and an attribute match score for each side using match formulae and computes a final score of the multi-directional match between the item profiles.