Behavioral Analytics Platform for Accurate User Matching
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Solution Overview
Problem
Existing online dating services rely on self-reported personal preferences and questionnaire responses, which can be inaccurate due to lack of self-awareness, psychological dissonance, and strategic self-presentation, leading to inefficient and inaccurate matching algorithms that waste computing resources and result in unsuccessful matches.
Innovation Solution
A behavioral analytics platform uses machine learning techniques to classify user profiles based on historical activity data from various sources, including transactional data, to predict affinities between users, providing more accurate and efficient matching by analyzing actual behavioral tendencies rather than self-reported information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-reported personal preferences and questionnaire responses are used for matching, then the system is simple to implement, but the matching accuracy deteriorates due to lack of self-awareness, psychological dissonance, and strategic self-presentation
Solution Approach 1:
The patent introduces behavioral data as an intermediary that mediates between users and the matching algorithm. Instead of directly using self-reported preferences, the system uses objective behavioral data from user activities, transactions, and interactions to infer true preferences and characteristics, thereby resolving the contradiction between implementation simplicity and matching accuracy
Solution Approach 2:
The patent replaces the mechanical system of questionnaires and self-reporting with an automated behavioral analysis system. Machine learning algorithms automatically analyze user behavior patterns from digital footprints, transactions, and platform interactions, substituting the manual self-reporting process with an objective, data-driven approach that improves accuracy without significantly increasing implementation complexity
2Measurement precision
If comprehensive behavioral data analysis is performed, then matching accuracy improves, but computing resource consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and categorizing behavioral data into meaningful patterns before the actual matching process. The system pre-analyzes user behavior to create behavioral profiles, preference models, and compatibility metrics in advance, so that during the matching process, the system only needs to perform efficient comparisons rather than comprehensive real-time analysis, thereby reducing computing resource consumption while maintaining high accuracy
Solution Approach 2:
The patent changes parameters by transforming raw behavioral data into standardized behavioral metrics and compatibility scores. The machine learning model converts diverse behavioral inputs into normalized parameters that can be efficiently compared and processed, reducing the computational complexity of the matching process while preserving the accuracy benefits of comprehensive behavioral analysis
Data Source
AI summary
A behavioral analytics platform may obtain a first data set associated with a first user and a second data set associated with a second user. The behavioral analytics platform may determine a first set of behavioral categories to classify the historical user activity data associated with the first user based on a first set of behavior vectors and determine a second set of behavioral categories to classify the historical user activity data associated with the second user based on a second set of behavior vectors. The behavioral analytics platform may populate one or more user interfaces that are accessible to the first user and/or the second user based on one or more values representing a degree to which the first set of behavioral categories and the second set of behavioral categories correspond to complementary behavioral tendencies, which may be determined using a machine learning technique.