Region Interest Recommendation Using Access Data Correlation
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Solution Overview
Problem
Current methods for determining the relationship between regions rely on expert knowledge and questionnaire collection, leading to inefficiencies, high costs, and low accuracy due to subjective factors.
Innovation Solution
A method that acquires access data to calculate correlation between regions based on user access patterns and map data, eliminating the need for manual questionnaire collection, and uses vector coding and graph embedding to quickly recommend regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and questionnaire collection are used to determine region relationships, then the method can capture subjective human understanding of regions, but the process becomes inefficient, costly, and less accurate due to manual involvement
Solution Approach 1:
The patent replaces the manual mechanical process of expert knowledge collection and questionnaire administration with an automated computational system. The system uses map data, user access data, and graph embedding algorithms to automatically calculate region correlations, eliminating the need for human experts to manually assess and record region relationships. This substitution of mechanical human processes with automated computational processes simultaneously improves efficiency (by removing manual bottlenecks) and accuracy (by eliminating subjective biases).
Solution Approach 2:
The system enables region relationship determination to be self-service, where the computational system automatically processes map data and user access patterns without requiring external expert intervention. The algorithm autonomously constructs graph representations, calculates correlations based on user behavior data, and generates region relationship determinations independently, making the process self-sufficient and eliminating dependency on manual questionnaire collection and expert analysis.
2Reliability
If manual questionnaire collection is used to gather region relationship data, then the data can reflect expert judgment, but the cost and time required for data collection increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing map data into graph representations and pre-establishing the computational framework for correlation analysis. User access data is continuously collected and pre-processed in real-time, so that when region relationship determination is needed, the system can immediately utilize the pre-prepared data structures and historical access patterns, eliminating the need for time-consuming manual questionnaire collection at the moment of need.
Solution Approach 2:
The patent replaces the manual mechanical process of questionnaire collection with automated data extraction from user behavior logs and map databases. The system automatically queries user access data, processes it through graph embedding algorithms, and generates region relationship determinations without human intervention, dramatically reducing both the time and cost of data collection while maintaining data quality through objective, consistent processing.
3Adaptability or versatility
If subjective factors are involved in region relationship assessment, then expert intuition can be captured, but accuracy decreases due to bias and inconsistency
Solution Approach 1:
The patent replaces the subjective mechanical process of expert intuition with an objective computational system. Instead of relying on individual expert judgment, the system uses graph embedding algorithms to objectively calculate region correlations based on user access data and map structures. This substitution eliminates subjective bias and inconsistency while capturing the essence of region relationships through data-driven, reproducible calculations that can be consistently applied across different contexts.
Solution Approach 2:
The system transforms the abstract concept of expert intuition into concrete measurable parameters through graph embedding. User access patterns, spatial relationships, and temporal behaviors are converted into quantifiable correlation scores and vector representations. This parameter transformation allows the system to capture the nuanced understanding that experts would have intuitively, but expresses it in objective, measurable terms that eliminate subjectivity and enable precise, consistent region relationship determination.
Data Source
AI summary
A method, device, and medium for recommending a region of interest are provided. The method includes: acquiring access data, the access data including correlation information between any two regions in a region group, in which a correlation between any two regions in the region group is acquired based on a region pair formed by the any two regions in the region group where sample users are located and the number of access times corresponding to the region pair, and in which the region group is acquired based on division of map data, and the map data includes boundary information of an entity in a real world; determining a region where a first user is currently located; and recommending region information of the region of interest for the first user based on the access data and the region where the first user is currently located.