POI Spatial Relationship Modeling for Higher-Order Neighbor Representation
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Solution Overview
Problem
Current methods for determining point of interest (POI) representation information fail to adequately capture complex relationships between POIs, leading to information loss and insufficient mining of relevant data.
Innovation Solution
A method involving the acquisition of a POI relationship graph, node relationship sets, and spatial distance matrices to determine POI representation information, utilizing graph neural networks for enhanced feature extraction and relationship perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relative distances between neighbor nodes are directly fused, then the processing is simple and fast, but information loss occurs and complex relationships between POIs are ignored
Solution Approach 1:
The patent segments the POI relationship analysis into multiple hierarchical levels: first-order neighbor nodes, second-order neighbor nodes, and higher-order nodes. Each level is processed separately through graph neural network layers, allowing complex relationships to be captured systematically without information loss while maintaining processing efficiency through layered computation.
Solution Approach 2:
The patent extends the analysis from traditional first-order neighbor relationships to second-order and higher-order neighbor relationships by adding dimensional depth to the graph structure. This multi-dimensional expansion allows the model to capture indirect relationships and complex POI interactions that are invisible in first-order analysis, thereby reducing information loss.
2Productivity
If only first-order neighbor nodes are considered, then the computation is efficient, but complex relationships and long-distance spatial features are ignored
Solution Approach 1:
The patent implements dynamic neighbor node selection where the model adaptively determines which neighbor nodes (first-order, second-order, or higher) to include based on the specific POI relationship context. This dynamic approach allows the system to maintain computational efficiency for simple cases while automatically expanding to capture complex relationships when needed, thus balancing efficiency and adaptability.
Solution Approach 2:
The patent introduces intermediate representation layers that aggregate and transform relationship information from multiple neighbor orders. These intermediate representations serve as mediators that synthesize complex relationship patterns before final POI representation is generated, enabling the model to perceive complex relationships without excessive computational burden.
3Measurement precision
If second-order and higher-order neighbor nodes are analyzed, then comprehensive POI relationships are captured, but device complexity increases
Solution Approach 1:
The patent uses copying of the graph neural network layer structure across different orders of neighbor nodes. Instead of creating entirely new complex processing pathways, the same GNN layer architecture is replicated and applied to different neighbor orders (first-order, second-order, etc.), allowing comprehensive relationship analysis while maintaining modular and manageable model complexity through structural reuse.
Data Source
AI summary
A method for acquiring point of interest (POI) representation information, includes: acquiring a POI relationship graph, and acquiring a node relationship set of each node on the POI relationship graph based on the POI relationship graph, in which the POI relationship graph includes edges representing relationships between nodes of POIs, and the node relationship set includes relationship paths where the node reaches all second-order neighbor nodes via any one first-order neighbor node; acquiring a spatial distance matrix between the node and corresponding second-order neighbor nodes; and determining first POI representation information of the node based on the node relationship set of the node and the spatial distance matrix.