Server Region Mobility Graph for Precise Ad Targeting
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Solution Overview
Problem
Existing geofencing technologies for providing advertisements are inconvenient as they require direct setting of regions and lack precision in targeting areas around specific locations.
Innovation Solution
A server that uses a graph convolutional network (GCN) model to predict user movement patterns within a region, creating a region mobility graph based on road information and user movement data, allowing for the identification of high-probability areas for advertisement placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If geofencing technology is used to provide content to user devices, then content delivery capability is improved, but region setting convenience deteriorates as users must directly set the region to be advertised
Solution Approach 1:
The server acts as an intermediary between the content provider and the user device. Instead of requiring the user to directly set regions, the server automatically determines advertisement regions based on user movement patterns and location information, then delivers content accordingly. This mediator approach resolves the contradiction by maintaining content delivery capability while eliminating the need for direct user region setting.
2Ease of operation
If a region around a place is simply set as a region to be advertised, then ease of operation is improved, but measurement precision deteriorates as the region is not precisely targeted
Solution Approach 1:
The system uses feedback from user movement patterns and location data to continuously refine region determination. The server analyzes actual user behavior (feedback) to identify precise advertisement regions, rather than relying on simple predetermined areas. This feedback mechanism maintains ease of operation while significantly improving region targeting precision through data-driven region selection.
3Measurement precision
If user movement data is collected and processed to create mobility graphs, then region targeting precision is improved, but device complexity increases
Solution Approach 1:
The complex task of region determination is segmented into manageable components: (1) collecting location information, (2) identifying movement patterns, (3) constructing mobility graphs, and (4) determining advertisement regions. By segmenting the complex process into distinct stages handled by different system components, the patent reduces perceived complexity while maintaining high precision in region targeting through systematic data processing.
Data Source
AI summary
A server, a method for controlling thereof, and a method for controlling an electronic apparatus are provided. The method for controlling the server according to an embodiment includes: obtaining road information in a region having a predetermined range and information on a plurality of places in the region; obtaining a region mobility graph corresponding to the region, based on movement information of at least one user between the plurality of places, the region mobility graph including a plurality of nodes corresponding to the plurality of places and an edge connecting the plurality of nodes; learning the region mobility graph, by using a graph convolutional network (GCN) model for predicting a relationship between the plurality of nodes in the region mobility graph; and providing the learned region mobility graph to an external apparatus.