Multi-Arm Bandit Arbitrator for Adaptive ML Prediction
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Solution Overview
Problem
Supervised machine learning algorithms face challenges in domains like customer selection and marketing due to the 'cold start' problem, where insufficient historical data and feedback hinder model training, especially when product lifespans are short, making it difficult to wait for enough data to aggregate before deploying systems.
Innovation Solution
A system utilizing a multi-arm bandit technique, such as Thompson Sampling, to select the best prediction by identifying customer segments, generating statistical models, and competing between machine learning models to solve business problems in real-time, adapting over time with online feedback to maximize key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning algorithms are used to select actions for customers, then prediction quality can be improved with sufficient historical data, but the system cannot operate during the cold-start period when insufficient historical data is available
Solution Approach 1:
The patent segments the prediction system into multiple independent models (supervised machine learning models and unsupervised statistical models) that can operate independently. Each model type handles different data availability scenarios, allowing the system to function during cold-start periods while maintaining prediction quality when data is available
Solution Approach 2:
The patent introduces an intermediary component that selects between supervised machine learning models and unsupervised statistical models based on data availability. This intermediary enables smooth transition between different operational modes, allowing the system to use statistical models during cold-start periods and switch to supervised models when historical data becomes available
2Measurement precision
If supervised machine learning algorithms are deployed, then accurate predictions can be achieved with sufficient feedback data, but the model training cannot be completed before the use case expires due to short product lifespans
Solution Approach 1:
The patent performs preliminary action by pre-training multiple supervised machine learning models offline before deployment. These pre-trained models are stored and can be immediately activated when their corresponding use cases become active, eliminating the need for online training during the short use case lifespan
Solution Approach 2:
The patent changes the operational parameters of the machine learning system by switching between offline pre-training mode and online inference mode. The system accepts pre-configured models with fixed parameters from offline training and uses them for rapid online predictions without requiring lengthy retraining during the short use case duration
3Adaptability or versatility
If a hybrid system combining multiple machine learning models and statistical models is implemented, then adaptability to different data availability scenarios is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic model selection mechanism that automatically adjusts which models are active based on real-time data availability conditions. The system dynamically switches between supervised and unsupervised models without requiring complex manual configuration or intervention
Solution Approach 2:
The hybrid system performs self-service by automatically selecting the appropriate model type based on the current operational context and data availability. The system monitors its own state and autonomously decides which models to use, eliminating the need for external orchestration or complex management infrastructure
Data Source
AI summary
A system, method, and computer program product are provided for generating intelligent automated adaptive decisions. In operation, a system receives a request to generate a prediction associated with a business problem for a customer. The system identifies a customer segment from a plurality of customer segments to which the new customer is most closely associated. Additionally, the system identifies a statistical model associated with the customer segment. Moreover, the system selects a best prediction from a competing set of machine learning models and the statistical model, utilizing a multi arm bandit arbitrator applying a multi arm bandit technique to solve the business problem.