Machine Learning Modules for Real-Time User Option Personalization
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Solution Overview
Problem
Existing computer applications face challenges in presenting appropriate options to users at the right time, engaging users effectively, and providing personalized experiences in real-time, due to issues like cold start problems, data sparsity, and the need for manual intervention.
Innovation Solution
A computer system utilizing machine learning modules to predict option selection probabilities and confidence values, with sample balancing and heuristic methods to determine the most suitable options for users, incorporating processing of categorical, numeric, and time series data to personalize app content and enhance user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning modules are used to predict option selection probabilities and determine personalized options in real-time, then user engagement and personalization are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system divides the machine learning functionality into separate modules: a first machine learning module for predicting option selection probabilities and a second machine learning module for determining confidence values. This segmentation allows each module to specialize in specific tasks, improving personalization capability while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including a sample balancing module that preprocesses data before feeding it to the machine learning modules, and a determining module that acts as an intermediary between probability predictions and final option selection. These intermediaries simplify the overall system by handling specific subtasks and reducing the complexity burden on the core machine learning modules.
2Measurement precision
If sample balancing and heuristic methods are implemented to address data sparsity and cold start problems, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs sample balancing as a preliminary action before the machine learning modules process the data. By pre-balancing the samples to address data sparsity and cold start problems, the system improves prediction accuracy without requiring the main prediction algorithm to handle these issues during real-time processing, thus reducing overall processing time.
Solution Approach 2:
The patent applies heuristic methods as a partial solution alongside machine learning predictions. When data is sparse or cold start conditions exist, the heuristic methods provide reasonable predictions without requiring extensive computational resources, balancing accuracy requirements with processing time constraints.
3Productivity
If real-time personalization is achieved through multiple machine learning modules, then user engagement improves, but system resource consumption increases
Solution Approach 1:
The system segments the personalization process into distinct modules that can be executed efficiently: probability prediction, confidence determination, and option selection. This segmentation allows the system to process only necessary computations for each user interaction, improving user engagement while managing resource consumption through targeted processing.
Solution Approach 2:
The machine learning modules are designed to learn from user interactions and improve their predictions autonomously over time. This self-service capability allows the system to become progressively more efficient at personalization, reducing the computational resources required for each subsequent prediction while maintaining or improving user engagement levels.
Data Source
AI summary
A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.