Interest Graph Update via URL Classification
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Solution Overview
Problem
Existing systems face challenges in effectively leveraging location data to derive interest signals for generating interest graphs, which are crucial for personalization in mobile communication devices, as tying geographic coordinates to interests or "taste" signals is complex.
Innovation Solution
The method involves receiving location data from mobile devices, determining associated Uniform Resource Locators (URLs) for Points of Interest (POIs), classifying these URLs using taxonomies, and updating interest graphs with the derived classification data to provide real-time recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geographic coordinates are directly used to represent user interests, then the system can maintain simplicity in data structure, but the ability to accurately derive interest signals is insufficient
Solution Approach 1:
The patent introduces POI data and URL classification taxonomies as intermediary layers between geographic coordinates and interest signals. Location data is first mapped to POI entities, then POIs are classified into categories through URL taxonomies, finally deriving interest signals from these structured classifications. This multi-stage intermediary process transforms raw coordinates into meaningful interest indicators while maintaining system manageability.
Solution Approach 2:
The system segments the interest derivation process into distinct stages: (1) matching location data to POI entities, (2) retrieving URL classifications for POIs, (3) categorizing POIs into interest categories, and (4) aggregating category frequencies to generate interest signals. This segmentation allows each stage to be optimized independently and facilitates accurate interest signal derivation without overwhelming system complexity.
2Measurement precision
If comprehensive POI classification taxonomies are implemented to improve interest derivation accuracy, then interest signal precision improves, but system complexity and processing overhead increase
Solution Approach 1:
The patent employs a universal URL classification taxonomy that serves multiple functions: (1) classifying POIs into interest categories, (2) providing a standardized framework for diverse POI types, (3) enabling consistent interest signal derivation across different locations, and (4) supporting both detailed and aggregated interest analysis. This single taxonomy structure handles various POI classification needs without requiring separate classification systems for different scenarios.
Solution Approach 2:
The system implements partial classification by focusing on the most relevant POI categories for interest derivation rather than attempting to classify every possible POI attribute. The taxonomy is applied selectively to POIs that contribute to interest signal generation, avoiding unnecessary processing of irrelevant classification details while maintaining sufficient accuracy for interest inference.
3Speed
If real-time location data processing is performed to provide timely recommendations, then service responsiveness improves, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing POI classifications and URL taxonomies in structured databases before runtime. When location data arrives, the system only needs to perform matching and aggregation operations on pre-processed data, significantly reducing real-time computational requirements while maintaining fast response times for interest signal generation and recommendation delivery.
Data Source
AI summary
A system and method are provided, for example, for determining interests from location data, by receiving location data from a mobile device, using the location data to determine a uniform resource locator associated with a point of interest, determining classification data associated with the uniform resource locator, and updating an interest graph associated with the mobile device using the classification data. The updated interest graph may also be subsequently used and the classification data may be used to provide information to the mobile device in real-time, such as recommendations or suggestions.