Logistic Distance Function for POI Relevance Ranking
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Solution Overview
Problem
Existing mapping applications fail to effectively rank points of interest (POIs) within a specified geographical region or viewport, leading to POIs being considered less relevant when located beyond a distance threshold, despite still being relevant when within the boundaries of a bounding box or distance radius.
Innovation Solution
The implementation of a logistic distance function that ranks POIs as substantially equally relevant within a viewport, transitioning to less relevant along the viewport's border, and mostly irrelevant outside, using a mathematical form f(d) = max_boost * (1 + θ0 * e^θ1 * d, where θ0 and θ1 control the shape and position of the falloff, to separate relevant POIs from those outside the viewport.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distance threshold is used to rank POIs, then POIs beyond the threshold are considered less relevant, but this causes POIs within the viewport boundaries to be incorrectly ranked as less relevant
Solution Approach 1:
The patent changes the parameter used for ranking from a simple distance threshold to a logistic distance function that incorporates the viewport's actual geometric boundaries. This function takes into account the viewport's center point, radius, and boundary characteristics to dynamically adjust relevance scores, ensuring POIs within the viewport are ranked appropriately while maintaining distance-based differentiation.
Solution Approach 2:
The patent implements a dynamic ranking system where the relevance score is not fixed but varies continuously based on the POI's position relative to the viewport. The logistic distance function dynamically adjusts scores based on real-time calculations of distance from the viewport center and proximity to the viewport boundary, allowing the system to adapt to different viewport configurations and POI locations.
2Productivity
If POIs are ranked by distance from center, then nearby POIs are prioritized, but this fails to account for POIs near the viewport boundary that should be considered relevant
Solution Approach 1:
The patent introduces a composite parameter system that combines distance from center with distance to boundary in the logistic distance function. This allows the system to simultaneously consider both the proximity to the viewport center and the proximity to the viewport edge, creating a more nuanced relevance score that reflects the actual spatial relationship between POIs and the viewport.
Solution Approach 2:
The patent adds a second dimensional consideration by incorporating the distance to the viewport boundary as an additional factor in the ranking calculation. Instead of relying solely on radial distance from center, the system now evaluates POIs along two dimensions: distance from center and distance to boundary, creating a more comprehensive spatial assessment.
Data Source
AI summary
The subject technology provides embodiments for a relevance ranking algorithm of point of interests (POIs). Based on a query request from a user of a mapping application, the relevance ranking orders the matching POIs based on either a reverse logistic distance function or a logarithmic distance function that is used as a multiplier for a baseline score of each of the matching POIs. The multiplier is used to boost or discount the respective relevance of each of the matching POIs, especially with respect to a specified area of interest included in the query request or a location of a user included in the query request. A subset of the ranked POIs may then be selected for display to the user.