User Classification via Location History for Ad Targeting
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Solution Overview
Problem
Advertisers face challenges in effectively targeting users with relevant advertisements, as existing systems lack the ability to accurately classify users based on their location histories and interests, leading to inefficient ad delivery and campaign optimization.
Innovation Solution
A method that determines a user's location history, analyzes it to derive categories, and uses these categories to boost relevant content items, such as advertisements, for delivery, by receiving location information, comparing it with other histories, and applying weights to scores for candidate content items based on matching categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertisers use traditional ad delivery systems, then ads can be delivered to users, but the targeting accuracy is low and ad effectiveness is reduced
Solution Approach 1:
The patent segments users into distinct categories based on their location history data. By dividing the user base into segments with similar location patterns (e.g., frequent visitors to specific types of locations), the system achieves more precise targeting without requiring complex individual user analysis for each ad delivery decision
Solution Approach 2:
The patent performs preliminary classification of users into categories before ad delivery based on their location history. This pre-segmentation allows the system to quickly match ads to appropriate user groups without complex real-time analysis, improving both accuracy and efficiency
2Measurement precision
If the system analyzes detailed location history for each user, then ad targeting precision improves, but processing time and computational resources increase
Solution Approach 1:
By segmenting users into categories based on location patterns rather than analyzing individual detailed histories in real-time, the system achieves precise targeting while reducing processing time. Pre-computed location categories enable fast matching during ad delivery
Solution Approach 2:
The patent creates simplified representations of user location behavior in the form of location categories. These categorical copies capture essential targeting information without requiring storage or processing of complete detailed location histories, reducing computational burden while maintaining targeting precision
3Adaptability or versatility
If the system uses basic user demographics for ad targeting, then implementation is simple, but ad relevance to user interests is insufficient
Solution Approach 1:
The patent adds a new dimension to user classification by incorporating location-based behavioral data alongside traditional demographics. This additional dimension (location categories derived from movement patterns) provides deeper insight into user interests and preferences, enabling more relevant ad targeting without replacing existing demographic systems
Solution Approach 2:
The patent introduces location categories as an intermediary layer between raw location data and ad targeting decisions. This intermediary classification system translates complex location history into meaningful behavioral categories that bridge the gap between user movement patterns and ad relevance, simplifying the overall system architecture
Data Source
AI summary
This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of determining a location history for a user, where the location history includes a plurality of location data points for the user; analyzing the location history to derive a category for the user; associating the user with the category; and using the user's associated category to boost one or more candidate content items for delivery to the user.