Personalized Hyperparameter Tuning via Contextual Multi-Arm Bandit
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Solution Overview
Problem
Conventional hyperparameter tuning techniques often fail to account for individual user preferences, leading to suboptimal performance of machine learning (ML) based algorithms in providing user experiences across a large user base.
Innovation Solution
The implementation of a personalized online hyperparameter tuning system using a contextual multi-arm bandit (CMAB) model or a reinforcement learning (RL) model to dynamically select hyperparameter values based on user responses, thereby optimizing the performance of ML based algorithms for individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional hyperparameter tuning techniques are used, then the system can provide user experience to a large user base, but the performance of ML based algorithms becomes suboptimal due to failure to account for individual user preferences
Solution Approach 1:
The patent implements dynamic hyperparameter tuning where the system continuously adapts hyperparameter values based on real-time user responses. The hyperparameter tuning ML model receives user response data and dynamically adjusts hyperparameters for each user individually, transforming the static tuning approach into a dynamic, personalized process that improves both adaptability and performance reliability
Solution Approach 2:
The patent establishes a feedback loop where user responses to ML algorithm outputs are collected and fed back into the hyperparameter tuning process. The hyperparameter tuning ML model uses this feedback information to refine and update hyperparameter settings for each user, enabling continuous optimization of algorithm performance based on actual user behavior and preferences
2Reliability
If personalized hyperparameter tuning is implemented, then the performance of ML based algorithms improves for individual users, but the system complexity increases due to real-time adaptation requirements
Solution Approach 1:
The patent employs a universal hyperparameter tuning ML model that can handle multiple users and various algorithm types through a single integrated framework. This multi-functional model processes diverse user responses and applies appropriate tuning strategies across different scenarios, reducing overall system complexity while maintaining high performance across individual users
Solution Approach 2:
The patent uses a simplified representation approach where the hyperparameter tuning ML model creates abstracted versions of user preferences and algorithm states. By working with these simplified copies rather than raw complex data, the system reduces computational complexity while still achieving personalized tuning results for each user
3Productivity
If real-time hyperparameter optimization is performed, then user experience is optimized in real-time, but the computational resources and time required for tuning increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing user response data and pre-calculating potentialhyperparameter adjustments before they are actually needed. The hyperparameter tuning ML model anticipates optimal settings based on historical patterns and user profiles, reducing real-time computation requirements and enabling faster optimization without significant loss of tuning accuracy
Data Source
AI summary
Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.