Location Similarity via Position Transition Graphs
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Solution Overview
Problem
Existing systems face challenges in determining similar locations and titles based on position transition data in social networking services, as common metrics like population size and weather may not accurately reflect industry-specific similarities, leading to inadequate insights for users.
Innovation Solution
A method is implemented to construct directed weighted graphs for locations and titles, using transition data and Laplacian smoothing to determine similar locations and titles, which are then used to provide statistical insights to users, incorporating personalized PageRank algorithms and weighted combinations across different granularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If common metrics like population size and weather are used to determine location similarity, then the calculation is simple and fast, but the accuracy of industry-specific insights deteriorates
Solution Approach 1:
The patent changes the parameters used for location similarity from generic metrics (population, weather) to industry-specific parameters derived from position transition data. By constructing directed weighted graphs based on actual job movement patterns between locations, the system adapts the similarity measurement to reflect industry-relevant characteristics, thereby improving accuracy without requiring complex manual parameter specification
Solution Approach 2:
The patent introduces position transition data as an intermediary element that mediates between raw location data and similarity calculation. This intermediary layer captures industry-specific movement patterns and translates them into weighted graph edges, enabling the system to compute accurate industry-relevant location similarities while maintaining computational efficiency through graph-based algorithms
2Measurement precision
If industry-specific position transition data is used to determine location similarity, then the accuracy of insights improves, but the system complexity increases
Solution Approach 1:
The patent segments the location similarity problem into distinct components: (1) collecting position transition data, (2) constructing directed weighted graphs for different industries, (3) computing similarities using graph algorithms, and (4) applying Laplacian smoothing. This segmentation allows each component to be handled independently with appropriate algorithms, reducing overall system complexity while maintaining high accuracy
Solution Approach 2:
The patent creates a universal framework using directed weighted graphs that can accommodate multiple industries and location pairs simultaneously. The same graph construction and similarity computation methodology applies across different industries, eliminating the need for industry-specific customizations and reducing system complexity through reusable components
3Measurement precision
If Laplacian smoothing and PageRank algorithms are applied to position transition data, then the accuracy of similar location identification improves, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing the directed weighted graphs from position transition data and pre-calculating the Laplacian matrices. These pre-computed structures are stored and reused for multiple similarity queries, avoiding redundant computations. The PageRank vectors are also pre-computed for each node, enabling fast similarity assessments without repeated expensive calculations
Solution Approach 2:
The patent implements dynamic computation strategies where the level of smoothing and PageRank iterations is adjusted based on query requirements. For queries requiring high precision, more iterations are performed; for approximate queries, fewer iterations suffice. This dynamic approach optimizes the balance between accuracy and computational time for different use cases
Data Source
AI summary
In an example, a granularity of location similarity is determined, wherein the granularity of location similarity is a level at which social networking data should be filtered to identify locations similar to a target location. Then a weighted graph of locations at the granularity of location similarity is constructed, wherein each node in the weighted graph is a location and a directed edge exists in the weighted graph between a first node and a second node if the social networking data, at the granularity of location similarity, indicates that a transition occurred wherein a member who resided at a location corresponding to the first node transitioned to reside to a location corresponding to the second node. The weighted graph of locations is traversed from a node corresponding to the target location in order to identify locations similar to the target location.