Offline Simulation for Content Page Policy Testing
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Solution Overview
Problem
Conventional content delivery systems face limitations in identifying valuable content for dynamic content pages due to the lengthy and potentially harmful process of A/B testing, which restricts the number of experiments that can be conducted and may negatively impact users if a new policy performs worse than the current one.
Innovation Solution
An offline simulation environment is created using a simulation application that runs on an experiment device, allowing for the testing of different action delivery policies and prediction models without affecting production systems, enabling parallel testing and reducing the risk of negative user impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If A/B testing is conducted in a live production system to identify valuable content, then the content selection can be optimized based on real user interactions, but the process is lengthy and may negatively impact users if a new policy performs worse than the current one
Solution Approach 1:
The patent applies preliminary action by conducting simulations offline before deploying policies to production. The simulation environment pre-evaluates multiple policies using historical data and predicted user interactions, allowing the system to identify optimal content selection strategies without lengthy live testing. This resolves the contradiction by performing the measurement and evaluation work in advance, reducing the time needed for actual A/B testing in production.
Solution Approach 2:
The patent creates a copy of the production environment as a simulation environment that replicates user behavior and system responses. This virtual copy allows for rapid testing of multiple policies without affecting real users or requiring long testing periods. The simulation copy enables fast evaluation of content selection policies while maintaining measurement precision through realistic user interaction modeling.
2Loss of information
If A/B testing is conducted in a live production system, then real user feedback can be obtained, but the number of experiments that can be conducted is limited and user experience may degrade
Solution Approach 1:
The patent creates a virtual copy of the production system that simulates user interactions and feedback. This simulation environment can run multiple experiments in parallel without consuming real user traffic or degrading user experience. The copy preserves the essential characteristics needed for obtaining user feedback while enabling unlimited experimentation throughput.
Solution Approach 2:
The simulation environment acts as an intermediary between policy evaluation and actual user exposure. Instead of directly testing policies with real users, the system uses the simulation as a mediator to predict user feedback and policy performance. This intermediary layer enables rapid experimentation while preserving real user feedback for final validation.
3Reliability
If a new policy is tested in a live system and performs worse than the current policy, then real-world impact can be observed, but negative impact on users occurs and the impact is irreversible
Solution Approach 1:
The patent applies preliminary anti-action by using the simulation environment to identify and prevent poorly performing policies before they are deployed to production. The simulation evaluates policies and predicts negative outcomes, allowing the system to reject suboptimal policies before they reach real users. This prevents harmful effects rather than correcting them after deployment.
Solution Approach 2:
The simulation environment serves as a safe copy where policy failures have no real-world consequences. Poorly performing policies can be identified and discarded in the simulation without impacting actual users. This copy enables reliable policy performance validation while eliminating the harmful factor of negative user impact during the testing phase.
Data Source
AI summary
A testing environment in which offline simulations can be run to identify policies and/or prediction models that result in more valuable content being included in content pages is described herein. For example, the offline simulations can be run in an application executed by an experiment device using data gathered by a production content delivery system. The simulation application can test any number of different policies and/or prediction models without impacting users that use a production content delivery system to request content.