Context-Enriched Machine Learning for Adaptive User Engagement
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Solution Overview
Problem
Existing machine learning models lack the ability to assess and respond to user inputs that indicate changes in user behavior or provide personalized experiences, leading to decreased user engagement, particularly in educational settings.
Innovation Solution
A system utilizing multiple machine learning models to analyze user inputs, detect parameters, generate context-enriched data classes, and customize graphical user interfaces to enhance user engagement by providing personalized content and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are trained from standard training data, then the model can generate basic outputs, but the model cannot assess user behavior changes or provide personalized experiences, leading to decreased user engagement
Solution Approach 1:
The system performs preliminary actions by detecting user behavior parameters and enriching training data with contextual information before generating model outputs. This allows the model to assess user behavior changes proactively rather than reactively, improving adaptability while preventing loss of behavioral information.
Solution Approach 2:
The system implements feedback mechanisms where user interactions and behavior data are continuously collected, analyzed, and fed back into the model training process. This creates a closed-loop system that enhances the model's ability to assess user behavior while maintaining information about user preferences and patterns.
2Ease of operation
If the machine learning model provides generic outputs based on training data, then the system is simple to operate, but user engagement decreases due to lack of personalization
Solution Approach 1:
The system employs self-service mechanisms where the machine learning model automatically detects user behavior patterns, generates personalized content, and adapts to user preferences without requiring manual configuration. This maintains ease of operation while enhancing personalization capabilities through automated adaptive processes.
Solution Approach 2:
The system changes parameters by dynamically adjusting model outputs based on detected user behavior parameters. The model modifies content generation parameters, data class selections, and display configurations in response to user interactions, enabling personalization while keeping the system simple to operate through automatic parameter adjustment.
3Adaptability or versatility
If multiple machine learning models are used to analyze user inputs and customize content, then user engagement increases through personalization, but the system complexity increases
Solution Approach 1:
The system applies universality by designing machine learning models that perform multiple functions: detecting user behavior, generating data classes, creating personalized content, and adjusting display parameters. This multi-functionality reduces the need for separate specialized models, maintaining adaptability while managing system complexity through consolidated model architecture.
Solution Approach 2:
The system segments the complex task of personalization into distinct functional components: user input analysis, parameter detection, data class generation, content customization, and display adjustment. Each segment is handled by specific model components working in sequence, making the overall complex system manageable through modular functional decomposition.
4Ease of manufacture
If the training data does not include user behavior information, then the model training is straightforward, but the model cannot provide personalized user experiences
Solution Approach 1:
The system performs preliminary data preparation by collecting and enriching training data with user behavior context before model training begins. This preliminary enrichment ensures the model learns from comprehensive data including behavioral patterns, while keeping the training process straightforward through pre-processed ready-to-use training datasets.
Solution Approach 2:
The system introduces an intermediary data enrichment layer that bridges standard training data and user behavior information. This intermediary process adds contextual behavioral data to training datasets without requiring complete redesign of the training pipeline, maintaining training simplicity while preventing loss of behavioral context.
Data Source
AI summary
A method including receiving at least one input, detecting at least one parameter associated with a context, generating, by a machine learning model, a first set of data classes enriched with the context, determining if each data class is associated with a data class repository to define a subset of data classes not associated with a data class repository, and generating, by a machine learning model, at least one data class repository for each data class. The method includes generating a display signal to display information associated with at least one data class repository, the display signal associated with a graphical user interface, altering, using a machine learning model, the display signal by altering at least one portion of the graphical user interface associated with the context, and sending the display signal to display the at least one portion of the graphical user interface on the user device.