Match Relevance Ranking Using Behavioral Interaction Data
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Solution Overview
Problem
Match-making systems often provide suboptimal matches based solely on user profiles, leading to user dissatisfaction, as they fail to consider behavioral interactions and relevance between entities.
Innovation Solution
The system determines the relevance of matches by using behavioral features such as interactions between entities, including viewing profiles and message exchanges, and assigns weights based on the probability of relevance to rank potential matches, utilizing a ranking function trained on these interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matches are determined based solely on user profiles, then the system is simple to operate, but the match accuracy is insufficient
Solution Approach 1:
The patent transitions from two-dimensional profile-based matching to three-dimensional matching by incorporating behavioral interaction data as a new dimension. The system combines profile attributes with behavioral features (profile views, message sends, response rates) to create a more comprehensive matching framework that captures dynamic interaction patterns rather than static characteristics alone.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user behavioral interactions and using this information to refine match recommendations. The feedback loop captures real-time engagement data (profile views, message exchanges, response patterns) and feeds this information back into the matching algorithm to continuously improve match accuracy based on actual user behavior rather than relying solely on initial profile data.
2Measurement precision
If behavioral features are incorporated into match determination, then match relevance improves, but data collection complexity increases
Solution Approach 1:
The system employs self-service data collection by automatically capturing behavioral information from user interactions with the platform itself. Rather than requiring separate data collection mechanisms, the system leverages existing interaction data (profile views, message sends, responses) that users naturally generate during normal platform usage, eliminating the need for additional sensors or manual data gathering processes.
Solution Approach 2:
The patent makes the data collection system universal by using the same infrastructure for multiple purposes: profile storage, interaction tracking, and match evaluation. The existing platform components serve dual functions of both facilitating user interactions and collecting behavioral data for match determination, reducing the need for separate dedicated data collection systems.
3Measurement precision
If multiple behavioral features are used for ranking, then match quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary computation by pre-calculating and storing behavioral feature metrics during user interactions. Rather than computing all match evaluations in real-time, the system prepares and caches behavioral data (profile view counts, message statistics, response patterns) in advance, so that when match ranking is needed, only the final aggregation and comparison steps are required, significantly reducing processing time.
Data Source
AI summary
Disclosed are methods and apparatus for automatically determining the relevance of matches between entities. A set of one or more indicators of relevance for each of a plurality of matches may be detected, where each of the plurality of matches exists between a first entity and a different one of a plurality of entities. Each set of one or more indicators of relevance indicates a degree of two-way interest for a corresponding one of the plurality of matches, the degree of two-way interest indicating both a degree of interest of the first entity in the corresponding one of the plurality of entities and a degree of interest of the corresponding one of the plurality of entities in the first entity. A probability of relevance of each of the plurality of matches may be determined based at least in part upon a corresponding set of one or more indicators of relevance. Each of the plurality of matches may be ranked based at least in part on the corresponding probability of relevance. A ranking function may be trained based upon the probability of relevance of each of the plurality of matches. The ranking function may subsequently be applied to identify and rank matches (e.g., in the absence of indicators of relevance).