Content Location Accuracy via Hash Distance Scoring
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Solution Overview
Problem
Conventional techniques for determining the precise location of content items in social networking systems often result in imprecise GPS coordinates, making it difficult to accurately identify specific locations due to inherent errors in location data.
Innovation Solution
A system and method that generate hash values for content items, calculate pairwise distance values with other content items grouped by location, apply a decay function to weight these distances, and sort scores to select candidate locations based on a threshold, providing a more precise location determination for evaluation content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS coordinates are used to determine location of content items, then location data can be obtained, but the precision is insufficient due to inherent errors
Solution Approach 1:
The patent introduces hash values as an intermediary representation of content items. Instead of directly comparing GPS coordinates or content features, the system computes hash values from content items and uses pairwise distance between hash values to indirectly determine location relationships. This intermediary approach transforms the unreliable GPS coordinate comparison into a more robust similarity-based location determination.
Solution Approach 2:
The patent transforms the location determination problem by changing the parameter space. Instead of working with geographic coordinates (latitude, longitude) directly, the system maps content items to hash value space and computes pairwise distances in this transformed space. The decay function further transforms the distance parameter to emphasize nearby content items, effectively changing how location proximity is measured and weighted.
2Measurement precision
If content items are grouped by location to improve accuracy, then the number of comparisons increases, but computational complexity increases
Solution Approach 1:
The patent segments the computational task into distinct phases: (1) grouping content items by their associated locations, (2) computing hash values for each content item, (3) calculating pairwise distances within each location group, and (4) applying decay functions and sorting to determine final locations. This segmentation allows the system to process content items in manageable chunks rather than performing all comparisons simultaneously, reducing peak computational complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-computing hash values for all content items and pre-grouping them by location before the actual location determination process. This preliminary organization of data structures ensures that when location determination is needed, the system only needs to compute pairwise distances within already-grouped content, rather than organizing and comparing all content items from scratch, significantly reducing computational complexity.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media are configured to generate a hash value of an evaluation content item. Hash values of a plurality of content items associated with location information are generated. A pairwise distance value between the hash value of the evaluation content item and a hash value of each content item of the plurality of content items assigned to a group of a plurality of groups is determined. A score for each group of the plurality of groups is generated based on a combination of distance values for the group. At least one location associated with the evaluation content item is determined based on scores for the plurality of groups.