Dynamic Radius Threshold Selection for Relevant Location-Based Content
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Solution Overview
Problem
Existing systems struggle to dynamically select an optimal radius threshold for displaying content items associated with business locations based on device location, leading to irrelevant content being shown to users.
Innovation Solution
A method and system that utilize machine learning to predict user interest in business locations by analyzing historical search query logs, sensor data, and performance metrics, adjusting the radius threshold based on features like popularity, query vertical, device location, and activity to ensure relevant content is displayed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed radius threshold is used for displaying content items, then the system complexity is reduced, but the relevance of content displayed to users deteriorates
Solution Approach 1:
The patent implements dynamic radius threshold selection that adjusts the search radius based on user context, device location, entity characteristics, and performance metrics. This transforms the static fixed-radius approach into a dynamic system that adapts to different scenarios, thereby improving content relevance without requiring overly complex manual configuration
Solution Approach 2:
The system changes the radius threshold parameter dynamically based on multiple factors including user location, entity popularity, query type, and historical performance data. By adjusting this key parameter adaptively rather than using a fixed value, the system achieves better content relevance while maintaining manageable complexity through automated parameter optimization
2Quantity of substance
If the radius threshold is increased to show more content items, then the quantity of content displayed is improved, but the precision of location-based relevance deteriorates
Solution Approach 1:
The radius threshold dynamically adjusts based on the specific query and context rather than using a static value. For example, popular entities or frequently searched locations may have larger effective radii, while less common entities have smaller radii, optimizing both content quantity and location precision simultaneously
Solution Approach 2:
Different radius values are applied to different entities based on their characteristics such as popularity, category, and historical performance. This localized approach allows the system to show more content for high-value entities while maintaining precision for location-sensitive queries, rather than applying a uniform radius to all entities
3Measurement precision
If the radius threshold is decreased to improve location precision, then the measurement precision is improved, but the quantity of relevant content items deteriorates
Solution Approach 1:
The system adjusts the radius parameter based on entity characteristics and query context. For popular or important entities, the effective search radius is increased to return more content items, while for less common entities, a smaller radius maintains location precision. This contextual parameter adjustment resolves the trade-off between precision and quantity
4Reliability
If dynamic radius selection is implemented to improve content relevance, then the content relevance is improved, but the computational complexity increases
Solution Approach 1:
The system pre-computes and stores performance metrics, entity characteristics, and historical data that inform radius selection. By preparing this data in advance rather than computing everything in real-time, the system reduces computational complexity during actual content selection while maintaining high relevance through informed dynamic radius adjustment
Solution Approach 2:
The system uses historical performance metrics and user interaction data to continuously refine radius threshold selections. This feedback mechanism allows the system to learn from past performance and optimize future radius choices, improving content relevance while reducing the need for complex real-time computations through data-driven decision making
Data Source
AI summary
Methods and systems for selecting content for a computing device are described. In some embodiments, the method comprises: receiving, by one or more processors of a data processing system, from a client computing device, location data of the client computing device; identifying, by the data processing system, an entity around a location of the client computing device; identifying, by the data processing system, a radius threshold corresponding to the entity based on a performance metric criterion; determining, by the data processing system, that a distance between the client computing device and the entity is less than the radius threshold corresponding to the entity; and transmitting, by the data processing system, responsive to the determination, to the client computing device, a content item associated with the entity.