Dynamic Matching Queries With Weighted Scoring for Accurate Pairing
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Solution Overview
Problem
Existing solutions for dynamic matching between different categories of participants are inefficient and burdensome, as participants are required to select from a list of candidates without a systematic approach to optimize pairings.
Innovation Solution
A computer-implemented method that dynamically defines queries with weights and relationships, generates recommendation scores based on participant responses, and identifies optimal matches using a recommendation engine, allowing real-time scoring and matching without overwhelming computational loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a systematic recommendation engine is implemented to generate optimal pairings, then matching accuracy is improved, but device complexity increases
Solution Approach 1:
The matching system is segmented into distinct functional modules: a query definition module that dynamically creates weighted queries, a response collection module that gathers participant answers, and a scoring module that generates recommendation scores. This segmentation allows each module to handle specific computational tasks independently, improving matching accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by dynamically defining queries and assigning weights to different criteria before the actual matching process. This pre-processing step establishes the scoring framework in advance, enabling accurate real-time recommendations without requiring complex computations during the matching phase itself.
2Productivity
If real-time scoring and matching is performed for all participant pairs, then productivity is improved, but use of energy increases
Solution Approach 1:
The system applies local quality by computing recommendation scores individually for each participant pair rather than performing exhaustive computations for all possible pairings simultaneously. Each scoring operation is localized to specific participant combinations, allowing real-time recommendations while distributing computational energy consumption across multiple smaller, manageable operations.
Solution Approach 2:
By pre-defining queries and their associated weights, the system eliminates the need to recalculate scoring criteria for each participant pair. This preliminary setup enables rapid real-time scoring computations with minimal energy consumption, as only the final score calculations require processing power rather than the entire matching framework.
3Adaptability or versatility
If dynamic query definitions with weights and relationships are implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements dynamics by allowing query definitions, weights, and relationships to be dynamically adjusted based on matching requirements. Queries can be created, modified, or removed on-the-fly, and weights can be reassigned to different criteria depending on the specific matching context. This dynamic capability provides high adaptability while the modular architecture manages complexity by confining dynamic changes to specific query definition modules rather than the entire system.
Data Source
AI summary
Techniques for dynamic matching include dynamically defining a first set of queries, including defining a plurality of weights for the first set of queries and defining one or more relationships between queries in the first set of queries. A first set of responses are received from a first participant in a first category of participants. A plurality of additional sets of responses are received from a second category of participants. A plurality of recommendation scores are generated, each corresponding to a potential pairing between the first participant and a participant in the second category of participants. Each respective recommendation score is generated based on: i) a comparison of the first set of responses and one of the additional sets of responses, ii) the defined plurality of weights, and iii) the defined one or more relationships. Matches are identified for the first participant based on the plurality of recommendation scores.