Surrogate Connectivity Graph Sampling for Revenue-Aware Recommendations
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Solution Overview
Problem
Conventional recommender systems are limited in efficacy as they prioritize accurate predictions over revenue or margin maximization, reinforce old patterns, and lack flexibility in modifying recommendation weighting or sampling, and are often built as 'black boxes' that do not allow external modification.
Innovation Solution
A dynamic surrogate connectivity graph is generated to model relationships between content portions, opportunities, and content providers, leveraging telemetry data to optimize path weights and generate recommendations that balance exploration and exploitation, using mean-variance portfolio allocation theory to maximize performance while minimizing variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommender systems are built to minimize loss function for accurate predictions, then prediction accuracy is improved, but revenue or margin maximization is worsened
Solution Approach 1:
The patent introduces a surrogate connectivity graph as an intermediary layer between the recommender system and the business objectives. This graph models relationships between content portions, opportunities, and content providers, allowing the system to translate prediction accuracy into business-value-aligned recommendations without modifying the core recommender algorithm.
Solution Approach 2:
The patent changes the optimization parameters from traditional loss function minimization to a multi-objective framework that includes both prediction accuracy and business metrics (revenue, margin). By adjusting the weighting parameters in the objective function and using telemetry data feedback, the system achieves both accurate predictions and revenue maximization.
2Reliability
If recommender systems reinforce old patterns through continuous top-list recommendations, then prediction consistency is improved, but adaptability to new patterns is worsened
Solution Approach 1:
The patent introduces dynamic elements into the recommendation system through time-decaying weights in the connectivity graph and adaptive sampling mechanisms. The surrogate connectivity graph is continuously updated with new telemetry data, allowing the system to dynamically adjust to new patterns while maintaining consistency through the structured graph architecture.
Solution Approach 2:
The system implements periodic sampling of recommendations from the surrogate connectivity graph rather than continuous reinforcement of the same top-list. This periodic action allows the system to revisit and re-rank recommendations based on updated telemetry data, maintaining adaptability while ensuring consistent prediction quality.
3Reliability
If access to internal code base of recommender system is restricted, then system security and stability are improved, but flexibility in modifying recommendation weighting is worsened
Solution Approach 1:
The surrogate connectivity graph serves as an intermediary interface that allows modification of recommendation weighting and sampling strategies without accessing the core recommender system code. Business logic can be implemented through the graph structure and sampling mechanisms, providing flexibility while maintaining system security and stability.
Solution Approach 2:
The patent segments the recommendation system into independent components: the core recommender algorithm (protected), the surrogate connectivity graph (modifiable), and the sampling mechanism (adjustable). This segmentation allows flexible modification of recommendation weighting through the graph and sampling layer without affecting the secure, stable core system.
4Device complexity
If conventional recommender systems use fixed recommendation weighting, then system simplicity is improved, but adaptability to different business contexts is worsened
Solution Approach 1:
The patent implements parameter changes in the recommendation weighting through the surrogate connectivity graph structure and sampling probabilities. These parameters can be adjusted based on different business contexts, opportunities, and content providers without changing the fundamental system architecture, maintaining simplicity while achieving adaptability.
Data Source
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.