Shared Cache Architecture for Cross-Experiment Action Consistency
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Solution Overview
Problem
Current online systems face challenges in tracking user behavior across different subgroups due to the need to maintain separate components and cross-component logic infrastructure when users transition between experiments, complicating the presentation of actions.
Innovation Solution
Implementing a shared cache system that stores user interactions and actions within a time-to-live window, allowing consistent action presentation across experiments for both new and existing users, thereby simplifying the system architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate components and cross-component logic infrastructure are maintained for different user subgroups, then each experiment can be independently managed, but the system complexity increases when users transition between experiments
Solution Approach 1:
The patent merges separate experiment components into a unified experiment framework. Instead of maintaining independent components for each experiment, the system uses a single experiment table that stores all experiment assignments and outcomes in one centralized location, eliminating the need for cross-component logic infrastructure while preserving experiment independence through logical separation of experiment definitions from execution infrastructure.
Solution Approach 2:
The experiment assignment service is designed as a universal component that handles multiple experiments simultaneously. The single experiment table serves multiple experiments, and the service can manage user assignments across different experiments without requiring separate infrastructure for each, thereby reducing overall system complexity while maintaining versatility.
2Reliability
If a shared cache system is implemented to store user interactions, then consistent action presentation is achieved across experiments, but additional memory resources are required
Solution Approach 1:
The shared cache system implements local quality by selectively caching only the specific user interaction data that is necessary for consistent action presentation across experiments. Rather than caching all user data, the system caches only experiment assignments and action presentations relevant to the unified experiment framework, optimizing memory usage while maintaining reliability.
3Loss of information
If multiple separate caches are maintained for different experiments, then experiment-specific data isolation is achieved, but the complexity of managing multiple caches increases
Solution Approach 1:
The patent combines multiple experiment-specific caches into a single shared cache system. The unified experiment table and shared cache eliminate the need for separate cache management for each experiment, reducing complexity while maintaining data isolation through logical separation of experiment records within the shared infrastructure.
Data Source
AI summary
An online system maintains a shared cache for storing actions assigned to users in different experiment groups. The system receives an indication that a user interacted with an online system, and data associated with the user. The system generates a set of propensities for a set of actions by identifying a first set of features for the user, accessing a first machine learning model, and applying the first machine learning model to the first set of features. The system selects an action based on the set of propensities and presents the action to the user. The system updates a cache of a set of user data and includes the transmitted action. The system receives a second indication and accesses a database to determine a selected action stored in association to the user. The system presents the selected action for a second time to the user.