Hybrid Temporal-Utility Classification for Engagement Systems
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Solution Overview
Problem
User engagement systems face inefficiencies and high operational loads due to the lack of a holistic, data-driven metric for determining the next best action for members based on their current needs, leading to repeated and irrelevant engagement actions.
Innovation Solution
A hybrid temporal-utility classification system is introduced, using machine learning models to determine a temporal classification and utility classification for predictive entities, which enables the selection of reliable and effective engagement actions by combining both broad population and member-specific data, thereby reducing the need for repeated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional engagement systems operate without a holistic data-driven classification metric, then they can maintain simpler system architecture, but they experience high operational loads and inefficiencies due to repeated and irrelevant engagement actions
Solution Approach 1:
The system segments the classification process into two distinct components: temporal classification (using temporal classification score generation ML model to determine temporal classification based on temporal classification score) and utility classification (using utility classification score generation ML model to determine utility classification based on utility classification score). This segmentation allows each component to specialize in specific aspects of member behavior prediction, improving overall productivity while managing complexity through modular architecture
Solution Approach 2:
The system performs preliminary classification of members into temporal-utility segments before engagement actions are executed. By determining hybrid temporal-utility classification in advance, the system pre-identifies which members are likely to respond to engagement actions, allowing operational resources to be focused only on high-probability targets rather than processing all members equally
2Reliability
If the system implements a hybrid temporal-utility classification system with multiple machine learning models, then engagement actions become more targeted and effective, but the computational requirements and processing time increase
Solution Approach 1:
The system applies partial classification by focusing computational resources on determining the most relevant classification dimensions for each member. Rather than exhaustively analyzing all possible attributes, the temporal classification score generation ML model and utility classification score generation ML model concentrate on specific temporal patterns and utility metrics that most strongly predict engagement response, reducing processing time while maintaining reliability
3Adaptability or versatility
If the system processes individual member data separately without population-level patterns, then member-specific customization is improved, but the system generates high operational loads due to lack of broad pattern recognition
Solution Approach 1:
The system incorporates feedback loops where engagement action results are fed back into the temporal classification score generation ML model and utility classification score generation ML model. This feedback mechanism allows the models to continuously learn from actual member responses, improving member-specific customization over time while the aggregated feedback from population-level patterns enhances overall operational efficiency by refining segmentation accuracy
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations by dynamically determining a hybrid temporal-utility classification for a predictive entity. The hybrid temporal-utility classification for the predictive entity may be determined based at least in part on outputs from a temporal score generation machine learning model and a utility score generation machine learning model.