Position Bias Correction Using Hierarchical Beta-Poisson Models
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Solution Overview
Problem
Existing systems for displaying content in electronic environments face challenges in accurately determining performance metrics due to position bias, where the position of content on a display significantly affects its perceived performance, leading to inaccurate ranking and revenue maximization, especially when insufficient data is available for specific items.
Innovation Solution
The use of a Beta-Poisson model with hierarchical priors and variational inference to correct for position bias by estimating the impact of content position on performance metrics, leveraging data from higher levels of the content hierarchy to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content is displayed at different positions to eliminate position bias, then measurement accuracy of performance metrics is improved, but data collection time and opportunity loss increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing position bias factors for different display positions before actual performance measurement. These pre-computed bias factors are then used to adjust observed performance metrics, eliminating the need to physically reposition content multiple times for calibration. This allows accurate performance measurement while minimizing data collection time and opportunity loss.
Solution Approach 2:
The patent introduces position bias factors as an intermediary variable that mediates between display position and observed performance. Instead of directly measuring true performance by eliminating position effects through physical repositioning, the bias factors serve as a mathematical intermediary that corrects observed metrics. This intermediary approach enables accurate measurement without the time-consuming process of collecting data from multiple positions.
2Measurement precision
If content is re-ranked or repositioned to collect sufficient data for bias correction, then measurement precision is improved, but system constraints and content relevance requirements are violated
Solution Approach 1:
The patent pre-computes position bias factors for all possible display positions using historical data or controlled experiments, storing these factors for later use. This preliminary calculation eliminates the need to dynamically reposition or re-rank content during normal operation. The pre-computed bias factors can be applied to any content at any position without violating ranking constraints or relevance requirements, thus maintaining system adaptability while achieving measurement precision.
Solution Approach 2:
The position bias factors act as a mathematical intermediary that decouples the measurement process from the content ranking system. Instead of physically repositioning content to collect bias data, the intermediary bias factors are computed separately and then applied to adjust performance metrics. This approach respects content relevance requirements and ranking constraints while still enabling accurate performance measurement through statistical correction.
3Device complexity
If performance data is collected from limited positions due to system constraints, then device complexity and operational overhead are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent uses position bias factors as mathematical intermediaries that compensate for limited data collection. Instead of requiring complex physical repositioning systems or extensive data collection infrastructure, the bias factors serve as a lightweight computational intermediary that corrects performance metrics. This approach maintains low device complexity while improving measurement precision through statistical adjustment rather than physical complexity.
Solution Approach 2:
The patent transforms the measurement problem from a physical space problem (requiring multiple positions and complex data collection) to a parameter space problem (using bias correction factors). By changing the approach from physical repositioning to parameter adjustment, the system achieves measurement precision with simpler infrastructure. The bias factors are computational parameters that adjust observed metrics without requiring physical system changes.
Data Source
AI summary
The effect of position bias on content performance can be determined in part using artificial intelligence and computer learning. Performance data can include the frequency with which an action, such as a purchase, occurs in response to an instance of content being displayed. The content can be associated with a node at a lowest level of an offering hierarchy, and performance data from the various levels can be rolled up to higher level nodes to obtain sufficient data to for accurate position bias determinations. A bias model can be trained using the data from the various levels, where the training determines weightings for the bias determinations of each level. Once the position bias for an offer is determined, a normalized performance value can be obtained that does not include the effects of the bias. The normalized values can be used to select and place content based on actual performance.