Coranking Multi-Source Location Data for Proximity Scoring
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Solution Overview
Problem
Conventional systems fail to accurately model and predict device interactions using location data, as they do not effectively consider a user's frequent locations and propensity to travel, leading to the transmission of renderable data objects that are inconveniently distant from the user's frequent locations.
Innovation Solution
The method involves coranking explicit and implicit location data sets to determine a coranked locations data set, which is then used to identify proximate renderable data objects by comparing their proximity to object redemption locations, thereby accounting for both fixed and frequent user locations and willingness to travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use basic location data modeling, then system complexity is low, but accuracy in predicting device interactions and user behavior is poor
Solution Approach 1:
The system segments location data into two distinct categories: explicit location data (from user-provided addresses, billing information, and profile data) and implicit location data (from device GPS, network location services, and application-generated location information). This segmentation allows the system to process and analyze different types of location information separately, improving prediction accuracy while maintaining manageable system complexity through specialized handling of each data type.
Solution Approach 2:
The system merges explicit and implicit location data through a coranking process that combines both data sources into a unified location profile. By integrating user-provided explicit locations with device-generated implicit locations and applying correlation analysis, the system achieves more accurate prediction of device interactions and user behavior patterns than either data source could provide alone.
2Reliability
If the system only uses current location data, then data processing is simple, but it fails to account for user's frequent locations and travel propensity
Solution Approach 1:
The system performs preliminary correlation analysis between explicit and implicit location data to identify frequent locations and user travel patterns before making predictions. By pre-processing the location data to establish correlated location profiles and identify user behavior patterns in advance, the system improves reliability of behavior prediction while avoiding the need for complex real-time analysis during device interactions.
Solution Approach 2:
The system uses feedback from the correlation analysis of location data to continuously refine its understanding of user frequent locations and travel propensity. By analyzing patterns in both explicit and implicit location data and feeding this information back into the modeling process, the system improves its prediction accuracy for user behavior while maintaining a structured approach to data processing.
3Ease of operation
If the system transmits renderable data objects without considering user location patterns, then transmission speed is fast, but user convenience is poor due to distant object locations
Solution Approach 1:
The system applies local quality by focusing analysis on geographically relevant locations - specifically, it identifies and prioritizes locations that are frequently visited by the user based on correlated explicit and implicit location data. By concentrating computational resources on analyzing and predicting behavior at these specific local locations rather than uniformly processing all possible locations, the system improves user convenience through location-aware data object transmission while controlling overall system complexity.
Data Source
AI summary
A method, computer program product, and apparatus for determining improved data objects are provided. An example method receives a request for a renderable data object from a location source device associated with a user profile including instant location data. The method queries an explicit locations database and an implicit locations database and coranks this multisource locations data. The method compares the coranked locations with one or more object redemption locations of each renderable data object. The method determines a proximity score for each renderable data object and ranks each renderable data object based upon proximity score. The method then identifies a proximate data object having a minimum proximity score and transmits the proximate data object to the location source device.