Location-Based Push System Using Resident Area Analysis
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Solution Overview
Problem
Existing systems fail to fully exploit user interests and needs by not providing personalized information based on location, leading to a passive response mode and limited user interaction.
Innovation Solution
A method and system that record user areas, calculate relationship strength, and push personalized point of interest information when the user enters a new area, using a formula to determine the resident area and combining search, subscription, and behavior information to provide relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the server uses a passive response mode to provide information, then the system complexity is low, but the user experience and information relevance are insufficient
Solution Approach 1:
The server performs preliminary actions by recording user location history, calculating resident areas, and pre-processing point of interest information before users actually need it. This allows the system to quickly provide personalized recommendations when users enter new areas, improving user experience without requiring complex real-time processing during user interactions
Solution Approach 2:
The system enables self-service by automatically tracking user locations, determining resident areas through frequency analysis, and pushing relevant information without requiring users to actively search or request data. The server autonomously monitors user behavior patterns and delivers personalized content based on calculated relationship strengths
2Loss of information
If the server pushes generic information to all users, then the information coverage is broad, but the information relevance and personalization are poor
Solution Approach 1:
The system applies local quality by providing different information to different users based on their specific location patterns and resident areas. Each user receives personalized point of interest recommendations tailored to their individual behavior characteristics, rather than uniform generic information, thereby maximizing information relevance for each user segment
Solution Approach 2:
The server dynamically changes information parameters based on user location data, calculating relationship strength values that determine which point of interest information to push. The system adjusts information delivery parameters such as push timing, content type, and target areas based on analyzed user behavior patterns, achieving personalization through parameter optimization
3Measurement precision
If the server records and analyzes detailed user location data, then the personalization accuracy improves, but the data processing complexity and time consumption increase
Solution Approach 1:
The system skips time-consuming complex calculations by using frequency-based metrics to determine resident areas. Instead of performing sophisticated real-time analysis, the server efficiently processes location data by counting visits and applying simple relationship strength formulas, quickly identifying user patterns and delivering personalized information without excessive processing delays
Data Source
AI summary
The present invention proposes a pushing method based on location information, comprising: recording a plurality of areas passed by a user; obtaining a resident area of the user according to the frequencies at which the user uses an electronic map in the plurality of areas, and pushing, when it is judged that the user enters a new area from the resident area, point of interest information in the new area to the user according to the point of interest information about the user in the resident area. The method in the embodiments of the present invention fully exploits the interests of a user and performs personalized customization, may better meet and inspire the needs of the user, and is widely applicable and easy to expand. The present invention also discloses a pushing system and server based on location information.