Digital Slate Additive Decomposition for Offline Policy Evaluation
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Solution Overview
Problem
Conventional digital content recommendation systems are inefficient, inaccurate, and inflexible, requiring extensive computing resources and time-consuming testing to evaluate new policies, leading to irrelevant content distribution and rigidity in adapting to different environments.
Innovation Solution
The slate policy learning system utilizes slot-level density ratio summations for universal off-policy evaluation, performing additive decomposition to generate a predicted reward distribution for new slate recommendation policies, allowing flexible and efficient transformation of historical interactions into accurate performance distributions without additional online testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems perform extensive A/B testing to evaluate new recommendation policies, then measurement precision of performance metrics is improved, but use of energy and loss of time increase significantly
Solution Approach 1:
The system performs offline evaluation of recommendation policies using historical data before actual deployment. By pre-computing performance metrics through importance sampling and additive decomposition on stored interaction logs, the system eliminates the need for extensive online A/B testing, thereby reducing real-time computing resource consumption while maintaining estimation accuracy
Solution Approach 2:
The system creates virtual copies of recommendation policies and evaluates them against historical data representations. By constructing synthetic evaluation scenarios from past user interactions, the system can assess policy performance without replicating full-scale online experiments, thus reducing energy consumption while preserving measurement precision
2Measurement precision
If conventional systems conduct extensive online testing for new policies, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs all necessary performance evaluations offline using historical data before policy deployment. By pre-processing and analyzing past user interactions through importance sampling and additive decomposition, the system obtains accurate performance metrics immediately upon policy creation, eliminating time-consuming online testing phases
Solution Approach 2:
The system replaces the mechanical process of real-time A/B testing with computational methods that process historical data through importance sampling and additive decomposition. This substitution allows rapid policy evaluation through mathematical operations on stored data rather than through time-consuming live experiments with users
3Productivity
If conventional systems use additive decomposition for off-policy evaluation, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the reward function into slot-level components through additive decomposition. By breaking down the overall reward into independent slot contributions, the system can evaluate policies more efficiently through importance sampling at the slot level, then aggregate results to get overall policy performance, thereby improving productivity while keeping each component manageable
4Measurement precision
If conventional systems require extensive testing samples for slate recommendations, then measurement precision is improved, but quantity of substance (data requirements) increases
Solution Approach 1:
The system creates synthetic evaluation samples by importance sampling historical data according to the target policy's probability distribution. By weighting existing historical interactions based on their relevance to the new policy, the system generates effective evaluation samples without requiring actual new user interactions or extensive additional data collection, thus reducing the quantity of substance needed while maintaining precision
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for performing off-policy evaluations of slate recommendation policies through additive decomposition. In particular, in one or more embodiments, the disclosed systems receive historical data corresponding to digital slate recommendations performed by a first slate recommendation policy, with each slate recommendation comprising a plurality of digital slot recommendations. Additionally, in some embodiments, the disclosed systems generate a second slate action using a second slate recommendation policy conditioned on user context. Further, in some embodiments, the disclosed systems generate a plurality of importance weights by summing a plurality of slot-level density ratios generated by comparing the slate actions of the second slate recommendation policy to the slate actions of the first slate recommendation policy. In some embodiments, the disclosed systems apply the plurality of importance weights to generate a predicted reward distribution for evaluation of the second slate recommendation policy.