Geo-temporal Model for Predictive Mobile Ad Targeting
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Solution Overview
Problem
Existing mobile advertising technologies lack effectiveness in targeting users based on predicted device locations and interactions, leading to irrelevant advertising and inefficient use of resources.
Innovation Solution
A method using a geo-temporal model to predict device locations and user interactions, selecting and caching advertisements for presentation at specific times, while dynamically updating the sample design and model to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertising content is targeted based on current device location only, then advertising relevance to user context is improved, but advertising effectiveness for future user behavior is limited
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical device location data to build predictive models before the actual advertising delivery moment. The geo-temporal model predicts future device locations in advance, allowing the system to prepare and deliver advertisements proactively based on predicted rather than just current location, thereby improving advertising effectiveness.
2Measurement precision
If device location data is collected continuously at high frequency, then prediction accuracy is improved, but network bandwidth usage and processing burden increase
Solution Approach 1:
The system dynamically adjusts the data collection frequency based on the current needs of the predictive model. Rather than using a fixed high-frequency sampling rate, the system adapts the sampling interval according to device movement patterns, prediction confidence levels, and model update requirements, thereby maintaining prediction accuracy while reducing unnecessary network bandwidth consumption and processing burden.
3Measurement precision
If comprehensive device location data is collected and stored long-term, then model accuracy is improved, but storage requirements and data management complexity increase
Solution Approach 1:
The system implements a data lifecycle management strategy where historical device location data is systematically discarded after serving its predictive purpose. Once the geo-temporal model has extracted sufficient patterns from historical data to achieve acceptable prediction accuracy, older data is discarded to free up storage space and reduce management complexity. The model is periodically retrained with newer data to maintain accuracy without requiring indefinite retention of all historical records.
Data Source
AI summary
A method, system, and medium are provided for targeting advertisements to users of mobile devices based on geo-temporal models. Time-stamped location information is collected for a mobile device and a dynamic geo-temporal model is constructed and updated when new data is collected according to a sample design. Using the geo-temporal model, device location and instances of user interaction with the device can be predicted, and advertisements can be provided based on the predicted location. Advertisements can be cached on the mobile device for later presentation, and the sample design can be updated to improve efficiency and accuracy in the modeling system.