Telemetry-Weighted Surrogate Graphs for Recommendation Sampling
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Solution Overview
Problem
Conventional recommender systems are limited in maximizing revenue or margin due to inaccurate predictions and reinforcement of old patterns, and modifying their internal processes is undesirable.
Innovation Solution
A dynamic surrogate connectivity graph is generated to model relationships between content portions, opportunities, and providers, using telemetry data to optimize path weights and leverage mean-variance portfolio theory for optimal recommendation sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommender systems are used to minimize loss function for prediction accuracy, then prediction accuracy is improved, but revenue maximization and margin maximization are limited
Solution Approach 1:
The patent introduces a surrogate connectivity graph as an intermediary structure that mediates between the recommender system's predictions and the business objectives. This graph models relationships between content, users, and opportunities, allowing the system to translate prediction accuracy into revenue-maximizing recommendations by sampling from the graph rather than directly using model outputs.
Solution Approach 2:
The system changes the parameter space from direct prediction accuracy optimization to a multi-parameter optimization involving prediction accuracy, user engagement metrics, and revenue indicators. By sampling recommendations from the surrogate connectivity graph and weighting them based on multiple parameters, the system achieves both accurate predictions and revenue maximization.
2Reliability
If recommender systems continuously show popular content to reinforce patterns, then prediction accuracy for popular items is improved, but diversity and discovery of new patterns are reduced
Solution Approach 1:
The surrogate connectivity graph is dynamically updated with new user interactions and content relationships, allowing the system to adapt to emerging patterns while maintaining reliability for established content. The graph structure enables flexible reweighting of recommendations based on current data, balancing reinforcement of popular items with discovery of new patterns.
Solution Approach 2:
The system segments the recommendation space into different paths and categories within the surrogate connectivity graph. By sampling from different segments (e.g., popular content paths vs. emerging content paths), the system maintains reliable predictions for popular items while also exposing users to diverse new content, preventing over-reliance on single patterns.
3Productivity
If internal code base of recommender system is accessed to modify recommendation weighting, then recommendation optimization is improved, but system complexity and risk of modification errors increase
Solution Approach 1:
The patent extracts the recommendation optimization logic from the internal code base and places it in the surrogate connectivity graph structure. This allows modification of recommendation weighting and sampling without touching the core recommender system code, reducing system complexity and modification risks while maintaining optimization capabilities.
Solution Approach 2:
The system creates a copy of the recommendation process through the surrogate connectivity graph, which can be independently modified and sampled from without affecting the original recommender system. This copying approach enables safe experimentation and optimization of recommendation weighting while isolating changes from the core system logic.
Data Source
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AI summary
A method, computer program product, and computing system for processing a plurality of content portions and a plurality of opportunities associated with a recommender system. A dynamic surrogate connectivity graph is generated using the plurality of content portions and the plurality of opportunities. Telemetry data associated with the recommender model is processed and a plurality of weighted path scores are modeled using the dynamic surrogate connectivity graph and the telemetry data. The plurality of weighted path scores are provided to the recommender model for generating subsequent recommendations.