Relevance-Independent Position Effects Estimator for Digital Ranking
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Solution Overview
Problem
Accurately estimating position effects in online systems is challenging due to the correlation between position and relevance, leading to biased estimates and the non-linear relationship between position and click probability, which affects the performance evaluation of ranking models.
Innovation Solution
A relevance-independent position effects estimator using a regression discontinuity design (RDD)-based statistical estimation technique that generates estimates by identifying small changes in adjacent ranking scores, allowing for de-biasing of position effects and providing accurate click probability forecasts for different computing environment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional position effects estimation methods are used, then position effects can be estimated, but the estimates are biased due to correlation between position and relevance
Solution Approach 1:
The patent extracts and removes the position effect component from the observed click probability by using regression discontinuity design. It isolates the position effect by comparing items with infinitesimally different ranking scores that are assigned to different positions, thereby separating position effects from relevance effects and producing unbiased estimates.
Solution Approach 2:
The patent introduces ranking score differences as an intermediary variable to measure position effects. By focusing on pairs of items with very small score differences (close to zero), it creates a natural experiment where position assignment becomes the primary differentiator, allowing position effects to be estimated without confounding from relevance differences.
2Productivity
If position effects are estimated using conventional methods, then evaluation can be performed, but the non-linear relationship between position and click probability affects performance evaluation accuracy
Solution Approach 1:
Instead of trying to model the entire non-linear position-click probability relationship, the patent focuses on local linear approximation around the discontinuity point (score difference = 0). By examining only the local behavior at the threshold where position assignments change, it captures the essential position effect without needing to model the full non-linear relationship.
3Adaptability or versatility
If ranking scores are used to determine position assignments, then relevant items can be ranked, but items with similar scores may be assigned to different positions causing estimation bias
Solution Approach 1:
Instead of treating position assignment as the cause and click probability as the effect, the patent inverts the approach by using position assignment differences (caused by infinitesimal score differences) as the instrument to identify position effects. It reverses the typical causal direction by using the assignment mechanism itself as the source of variation for estimation.
Data Source
AI summary
Technologies for generating relevance-independent position effects estimates for a set of ranked digital items are described. Embodiments include creating an input data set that includes request tracking data and associated activity tracking data. A relevance-independent position effects estimator generates an output data set. An item of the output data set includes user interface position data associated with a pair of adjacently positioned items of the input data set. The user interface position data indicates that a change in user interface activity probability data relating to a change in position between the items of the pair is greater than a change in the user interface activity probability data relating to a difference in the relevance score between the items of the pair. The output data set is stored in a searchable data store. Data from the searchable data store is provided to a downstream service.