Location Graph User Profile Derivation for Ad Targeting
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Solution Overview
Problem
Existing advertising technologies fail to accurately target users with relevant content based on their location history, as they rely solely on current location data or browsing behavior, lacking a comprehensive approach to derive user attributes from past locations.
Innovation Solution
A location graph-based system that maps user location data to defined locations, assigning demographic and behavioral attributes, and updates user profiles over time, using both current and past location information, along with third-party data, to provide personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only current location data is used for ad targeting, then the system is simple and fast, but the accuracy and relevance of targeted ads deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing user location history data in advance, building a comprehensive location profile before ad targeting is needed. This allows the system to derive user attributes from historical location patterns rather than relying solely on current location, improving ad relevance while maintaining operational efficiency through pre-processed data.
Solution Approach 2:
The patent transitions from one-dimensional current location data to multi-dimensional location analysis by incorporating historical location trajectories, temporal patterns, and derived user attributes. This dimensional expansion enables more accurate user profiling and ad targeting by considering the full context of user movement patterns across time and space.
2Reliability
If comprehensive location history is collected and analyzed, then user profile accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by continuously collecting and organizing location history data in the background, pre-computing user attributes and location patterns before they are needed for ad targeting. This asynchronous preprocessing reduces real-time processing requirements while maintaining high profile accuracy.
Solution Approach 2:
The location graph structure enables self-service data organization where new location data automatically integrates into the existing graph structure, and user attributes are continuously updated without requiring full reprocessing of historical data. The system maintains accuracy efficiently by incrementally updating profiles as new location information becomes available.
3Adaptability or versatility
If user attributes are derived from location patterns, then ad relevance improves, but privacy concerns and data security requirements increase
Solution Approach 1:
The system extracts and stores only essential user attributes derived from location patterns (such as home location, work location, frequently visited places) rather than retaining complete detailed location history. This extraction approach maintains ad targeting relevance by preserving meaningful user characteristics while minimizing the retention of sensitive personal information.
Solution Approach 2:
The location graph serves as an intermediary structure that processes and anonymizes raw location data, transforming it into aggregated user attributes and patterns. This intermediary layer enables relevant ad targeting based on user behavior patterns while obscuring individual identifying information, thereby reducing privacy risks.
Data Source
AI summary
Location graph-based derivation of user attributes. Location data associated with a user, such as a current and/or past location at which the user has been, is received. A user attribute data associated with the location data is determined and used to update a user profile associated with the user.