Calibrated User Profile Generation for Marketplace Matching
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Solution Overview
Problem
Existing graphical user interface systems fail to effectively match buyers and sellers due to uncalibrated user profiles and lack of context in notifications, leading to inefficient communication and reduced user engagement.
Innovation Solution
The system generates calibrated user profiles and notification models based on subjective estimations of value and user actions, allowing for personalized matching and targeted notifications that consider time, location, and notification content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user profiles are generated directly from user-provided information, then the system can automatically populate information and save user effort, but the profiles are not calibrated to weight differing aspects of user information and cannot learn from user actions
Solution Approach 1:
The system enables self-service by automatically generating calibrated user profiles through machine learning algorithms that process user actions and interactions. The calibration module autonomously weights different profile aspects based on observed user behavior patterns, eliminating the need for manual user configuration while continuously adapting to user preferences through self-learning mechanisms.
Solution Approach 2:
The system implements feedback loops where user actions and interactions are continuously monitored and fed back into the calibration module. This feedback mechanism allows the system to adjust profile weights and improve matching accuracy over time, transforming static user-provided information into dynamic, calibrated profiles that reflect actual user preferences and behaviors.
2Productivity
If notifications are transmitted without context consideration, then the system can send blanket notifications to all users, but users receive irrelevant notifications and effectiveness is limited
Solution Approach 1:
The notification system applies local quality by customizing notification content, timing, and delivery channels based on individual user profiles and contextual factors. Instead of uniform blanket notifications, the system adjusts notification characteristics locally for each user according to their calibrated preferences, current location, device state, and interaction history, thereby improving both relevance and effectiveness.
Solution Approach 2:
The system performs preliminary action by pre-calculating optimal notification timing and channels based on user profiles before actually sending notifications. The calibration module prepares personalized notification strategies in advance, analyzing user behavior patterns and predicting optimal moments for contact, which increases the likelihood of user engagement while reducing unnecessary or mistimed notifications.
3Device complexity
If the system transmits notifications without regard to time, location, or content, then the notification system is simple to implement, but the lack of context limits notification effectiveness
Solution Approach 1:
The notification system implements dynamics by making notification parameters adaptive rather than static. The system continuously adjusts notification timing, channel selection, and content based on real-time user context and calibrated profile data. This dynamic approach allows the system to respond to changing user states and preferences, improving effectiveness without requiring overly complex manual configuration.
Data Source
AI summary
Systems and methods are presented for matching a buyer and a seller on a market place system and generating calibrated user profiles. In one such system a plurality of subjective estimations of value is received. The subjective estimations of value are a measure between a predetermined minimum value and a predetermined maximum value. A user profile is generated. A plurality of user actions corresponding to the plurality of subjective estimations of value is received. The user profile is calibrated based on the plurality of user actions.