POI Alias Determination via Query Behavior Log Analysis
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Solution Overview
Problem
Current methods for acquiring Point of Interest (POI) aliases, such as user-generated content, professionally-generated content, and web crawlers, face issues of low efficiency, high costs, and low coverage rates due to unreliable data quality and high manpower requirements.
Innovation Solution
A method and apparatus that generate a candidate alias list based on query behavior-associated logs, screening out target aliases through association relationships, improving the accuracy and efficiency of POI alias determination without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user-generated content method is used to acquire POI alias, then coverage rate is improved, but data quality becomes poor and manpower requirement increases
Solution Approach 1:
The system uses query behavior logs as feedback to automatically identify and verify POI aliases. By analyzing user query patterns and matching them with POI information, the system continuously refines the alias list without requiring manual review, thus maintaining high data quality while preserving broad coverage.
Solution Approach 2:
The system performs self-verification of POI aliases through automated query behavior analysis. Instead of relying on manual review or professional content creation, the system uses its own operational data (query logs) to validate and confirm aliases, eliminating the need for high-cost manual verification while ensuring data reliability.
2Reliability
If professionally-generated content method is used to acquire POI alias, then data quality is improved, but cost increases and coverage rate decreases
Solution Approach 1:
The system replaces expensive professional content creation with self-service automated extraction from query behavior logs. By using its own operational data, the system achieves both high data quality and broad coverage without requiring external professional resources, effectively eliminating the trade-off between quality and coverage.
Solution Approach 2:
Instead of creating new professional content, the system copies and analyzes existing query behavior data from user interactions. This approach leverages readily available operational logs to generate aliases, avoiding the high costs of professional content production while maintaining comprehensive coverage across all POIs.
3Ease of manufacture
If web crawler method is used to acquire POI alias, then cost is reduced, but coverage rate remains low due to sparse network presence
Solution Approach 1:
The system uses query behavior logs as an intermediary between user interactions and POI alias generation. Instead of directly crawling the web where POI information is sparse, the system analyzes the intermediate layer of query logs that contain rich alias information, thereby achieving high coverage without the costs and limitations of web crawling.
Solution Approach 2:
The system transitions from the traditional web crawling dimension to the query behavior log dimension. By analyzing data from a different dimension (user query patterns rather than web pages), the system discovers POI aliases that would be invisible to conventional web crawlers, significantly improving coverage while maintaining low cost.
4Reliability
If manual review method is used to verify POI alias, then data quality is improved, but manpower requirement increases
Solution Approach 1:
The system performs self-verification of POI aliases through automated query behavior analysis. By using its own operational data to validate and confirm aliases, the system eliminates the need for manual review while maintaining high data quality, thus reducing manpower requirements without sacrificing reliability.
Solution Approach 2:
The system continuously refines alias verification through feedback from query behavior patterns. By analyzing how users actually query and interact with POI information, the system automatically validates alias accuracy without human intervention, achieving both high data quality and reduced manpower requirements simultaneously.
Data Source
AI summary
Embodiments of the present disclosure provide a method, apparatus, computer device, and storage medium for determining a POI alias. The method may include: acquiring a to-be-processed target POI, and generating a candidate alias list corresponding to the target POI based on a query behavior-associated log matching the target POI; and screening out at least one target alias corresponding to the target POI in the candidate alias list, according to an association relationship between each candidate alias in the candidate alias list and the target POI.