Linear Stochastic Bandit Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional recommendation systems are limited by a fixed noise estimate threshold, which restricts their ability to fully exploit available data and leads to inefficient use of computing resources, resulting in suboptimal recommendations and reduced accuracy over time.
Innovation Solution
The implementation of a linear stochastic bandit technique that refines noise estimates and confidence intervals based on user interaction data, allowing for a dynamic tradeoff between exploration and exploitation, enabling the recommendation system to optimize digital content recommendations by selecting the most promising options with higher potential rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed noise estimate threshold is used in conventional recommendation systems, then the system ensures that the true noise value is contained within the estimate, but this limits exploitation and reduces recommendation accuracy over time
Solution Approach 1:
The patent transforms the fixed noise estimate threshold into a dynamic, adaptive threshold that evolves over time through online learning. The system continuously updates the noise estimate based on observed rewards and confidence intervals, allowing the threshold to adapt to changing data patterns while maintaining reliability bounds through theoretical guarantees.
Solution Approach 2:
The patent changes the noise estimate parameter from a static fixed value to a dynamic variable that is continuously refined based on observed data. By updating the noise estimate and confidence interval parameters online, the system improves measurement precision while maintaining reliability through bounded error guarantees.
2Reliability
If a fixed noise estimate threshold is used, then the system maintains conservative estimates, but this leads to inefficient use of computing resources and suboptimal recommendations
Solution Approach 1:
The system dynamically adjusts the noise estimate threshold based on accumulated data and confidence interval calculations. This allows the system to maintain conservative guarantees when needed while becoming more efficient over time as the estimate converges, optimizing the tradeoff between reliability and productivity.
Solution Approach 2:
The patent implements continuous online learning where the noise estimate is continuously refined with each new observation. This continuous adaptation allows the system to maintain reliability guarantees while improving computational efficiency over time, avoiding the static inefficiency of fixed thresholds.
3Measurement precision
If exploration is limited by a fixed noise estimate, then the system focuses on exploitation, but this reduces the ability to learn about the linear relationship between features and rewards
Solution Approach 1:
The system uses feedback from observed rewards to continuously update the noise estimate and confidence intervals. This feedback mechanism allows the system to balance exploration and exploitation dynamically, using the refined noise estimates to guide exploration while maintaining exploitation of known high-reward options, thereby reducing information loss.
4Reliability
If a conservative fixed threshold is used, then the system ensures noise containment, but this limits exploitation and reduces summed rewards over time
Solution Approach 1:
The patent implements a dynamic threshold that adapts based on confidence interval calculations and observed data. This allows the system to maintain noise containment guarantees while progressively improving exploitation efficiency, thereby increasing the summed rewards over time without sacrificing reliability.
Solution Approach 2:
The system changes the noise estimate parameter from fixed to dynamic, allowing it to be refined online based on observed rewards. This parameter change enables the system to maintain containment guarantees while improving exploitation, leading to higher summed rewards over time.
Data Source
AI summary
Recommendation systems and techniques are described that use linear stochastic bandits and confidence interval generation to generate recommendations for digital content. These techniques overcome the limitations of conventional recommendations systems that are limited to a fixed parameter to estimate noise and thus do not fully exploit available data and are overly conservative, at a significant cost in operational performance of a computing device. To do so, a linear model, noise estimate, and confidence interval are refined by a recommendation system based on user interaction data that describes a result of user interaction with items of digital content. This is performed by comparing a result of the recommendation on user interaction with digital content with an estimate of a result of the recommendation.