Machine Learning Ranking System with Dynamic User Feedback
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Solution Overview
Problem
Existing machine learning systems face challenges in accurately ranking entities based on dynamic user preferences and requirements, as they often rely on static training data and do not effectively incorporate real-time user input to update rankings and retrain models.
Innovation Solution
A system that uses a trained machine learning model to filter and rank entities using overall and ranking match scores, allowing user input to reorder interface elements, which updates the training data and reweights attributes to refine the ranking process, enabling more precise and relevant entity rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static training data is used to train the machine learning model, then the model can be trained efficiently with fixed parameters, but the system cannot adapt to dynamic user preferences and evolving requirements
Solution Approach 1:
The system transitions from static training data to dynamic training data that evolves with user interactions. The training data is continuously updated as users reorder interface elements, allowing the machine learning model to adapt to changing user preferences while maintaining its core functionality through iterative retraining.
Solution Approach 2:
User reordering actions serve as feedback signals that are incorporated into the training data. The system uses this feedback to retrain the machine learning model, creating a closed-loop system where user preferences directly influence model updates and subsequent ranking improvements.
2Measurement precision
If the system incorporates real-time user input to update rankings, then the relevance of entity rankings improves, but the system complexity increases due to continuous model retraining and data updates
Solution Approach 1:
Instead of completely retraining the model with all training data each time user input is received, the system uses partial retraining with updated or augmented training data. This approach achieves improved ranking accuracy while reducing computational overhead and system complexity compared to full retraining cycles.
Solution Approach 2:
The system pre-processes and structures training data in advance, organizing entity profiles, attributes, and user preferences into ready-to-use formats. This preliminary preparation reduces the complexity of real-time model updates by having data pipelines and processing routines already in place before user interactions occur.
3Measurement precision
If attribute weights are dynamically adjusted based on user feedback, then the ranking relevance to user preferences improves, but the computational resources required for model retraining increase
Solution Approach 1:
The system dynamically changes attribute weights based on user feedback patterns. By monitoring which attributes users prioritize when reordering entities, the system adjusts weight parameters in the machine learning model to reflect current user preferences, improving ranking relevance without requiring complete model restructuring.
Solution Approach 2:
The system performs partial model updates focusing only on the specific attribute weights affected by user feedback, rather than retraining the entire model. This selective approach reduces computational resource consumption while still achieving improved ranking relevance for the affected attributes.
Data Source
AI summary
Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.