Peer Directory Matching Algorithm Using Response History Bias
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Solution Overview
Problem
Existing peer directory systems often present too many irrelevant matches, wasting time for users as they sift through numerous potential contacts, and there is a low likelihood that selected peers will respond to connection requests, leading to inefficiencies in finding suitable experts for business or technical assistance.
Innovation Solution
An interactive peer directory system that uses a user interface to collect and index user profile information, employing a peer relevancy algorithm to assign weights based on categories like initiative, vendor, OS, industry, and firm size, and incorporates past connection response history to provide biased matches, ensuring more relevant and responsive peer connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prior art systems provide multiple potential matches to requesters, then the requester has more options to choose from, but the requester's time is wasted sorting through irrelevant matches and the likelihood of finding the best match decreases
Solution Approach 1:
The patent changes the parameter of match quality by introducing a scoring system that evaluates candidates based on multiple weighted criteria including expertise relevance, response history, and availability. This transforms the matching process from providing many unfiltered options to providing a ranked list of high-quality matches, reducing the time requesters need to spend evaluating candidates while maintaining adaptability through configurable weighting parameters.
Solution Approach 2:
The system performs preliminary filtering and scoring of candidate matches before presenting them to the requester. By pre-evaluating candidates against multiple criteria and ranking them in advance, the system eliminates the need for requesters to manually sort through irrelevant matches, directly addressing the time loss problem while preserving the ability to provide multiple relevant options.
2Reliability
If requesters wait several days to hear back from selected peers, then the peer may provide valuable advice, but the requester's time is wasted and the peer may not respond at all
Solution Approach 1:
The system implements feedback mechanisms by tracking peer response history and using this information to score and rank future match recommendations. Peers who respond quickly and helpfully receive higher scores, making them more likely to be recommended in future matching scenarios. This creates a positive feedback loop that improves both response likelihood and reduces waiting time over time.
Solution Approach 2:
The matching system is dynamic and adapts based on peer behavior patterns. By continuously updating peer profiles with response timing and quality data, the system can dynamically adjust match recommendations to favor responsive peers, thereby increasing reliability while reducing the time requesters wait for responses.
3Adaptability or versatility
If the system provides many potential matches, then the requester has more choices, but the quality of matches decreases as requesters settle for less relevant options
Solution Approach 1:
The system changes the parameter of match presentation by implementing a ranked listing based on composite scores derived from multiple weighted criteria. Instead of presenting all matches equally, the system transforms the data into a prioritized list where the most relevant matches appear first, maintaining adaptability through configurable weights while significantly improving match relevance accuracy.
Solution Approach 2:
The patent segments the matching process into distinct evaluation dimensions (expertise relevance, response history, availability, etc.), each scored and weighted separately. This segmentation allows the system to maintain multiple match options while ensuring high relevance by evaluating each candidate across multiple independent criteria before ranking them, preventing requesters from settling for less relevant options.
Data Source
AI summary
A system is provided for locating peers having a desired expertise. User profile information is stored in a profiles database. A search engine indexes the profiles database and appends appropriate profile tags to this information. A peer relevancy algorithm searches for candidate peers among the indexed user profile information. Weights are assigned to candidate peers based on different categories of the indexed user profile information, and peer matches are selected based on the assigned weights. In order to provide matches that are most likely to accept a connection request, data is maintained as to which potential peers have a history of accepting requests to connect and which have a history of refusing to connect. Potential matches are biased to favor those that have a tendency to accept connection requests. Contact information of requester and recipients are not disclosed until the recipient accepts the requester's connection request.