Real-time Matching System for Personalized Recommendations
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Solution Overview
Problem
Existing matching systems do not immediately process changes in data, leading to delayed updates in user actions and unavailable members being recommended to users, resulting in irrelevant results.
Innovation Solution
Implementing a real-time matching system that continuously learns from changes in user behaviors to update match models, providing immediate personalized recommendations by determining member availability and selecting suitable members based on search criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is collected in static data containers and processed at predetermined intervals, then system complexity is reduced, but matching accuracy deteriorates due to delayed updates
Solution Approach 1:
The patent transforms static data containers into dynamic data structures that continuously update member availability status in real-time. The system dynamically adjusts matching results based on current member states (available/unavailable) rather than relying on periodic batch updates, thereby improving matching accuracy without requiring complete system redesign
Solution Approach 2:
The patent implements continuous real-time processing of member availability changes through event-driven architecture. When a member's status changes (e.g., accepting a job, declining invitations), the system immediately processes this change and updates matching results, eliminating the delays associated with periodic batch processing while maintaining manageable system complexity through targeted updates
2Measurement precision
If changes in data are processed immediately, then matching accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts and processes only the critical availability status changes rather than processing all data changes. By focusing processing resources specifically on members whose availability status changes (the most impactful updates), the system achieves real-time accurate matching while minimizing unnecessary processing time for other data elements
Solution Approach 2:
The system pre-establishes real-time processing mechanisms and event handlers before changes occur. When availability changes happen, pre-configured processing logic immediately executes to update matching results, eliminating processing delays while maintaining high accuracy through prepared processing pathways
3Device complexity
If user actions are not immediately captured and analyzed, then system complexity is reduced, but relevance of recommendations deteriorates
Solution Approach 1:
The patent implements real-time feedback loops where user actions (accepting jobs, declining invitations, searching for work) are immediately captured, analyzed, and used to update member availability status. This continuous feedback mechanism ensures recommendations remain highly relevant to current user behavior patterns while managing complexity through targeted analysis of only action-based feedback signals
Data Source
AI summary
Embodiments of the present invention relate to real-time matching. As changes in data occur in a system, these changes are immediately used by running models implemented in the system to provide users with real-time personalized results. Concurrently, the system is continuously learning from these same changes to build test models, which are built upon the running models (which reflect past knowledge) using the current knowledge. The running models are continuously evolving as they are updated with the test models. A model implemented by the system is a match model, which is configured to intelligently determine which members are available for a new job and to provide a user with at least one member from that pool of available members who satisfies a search criteria specified by the user. The user's actions and the available member's actions are both immediately captured, analyzed and reflected in the system.