Location-Based App Recommendation via Crowd-Sourced Usage Analysis
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Solution Overview
Problem
Users face difficulty in selecting the most relevant application for their current location due to the vast number of applications available on digital distribution platforms for mobile devices.
Innovation Solution
Crowd-sourced, localized application usage data is collected from mobile devices and analyzed to determine the most relevant application for a specific location, which is then recommended to users through a network-based service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for relevant applications on digital distribution platforms, then they can find applications relevant to their current location, but the process is time-consuming and difficult due to the large number of available applications
Solution Approach 1:
The system performs preliminary analysis of application usage data from multiple mobile devices at specific locations before the user needs an application. By pre-processing crowd-sourced data to identify which applications are most relevant to each location, the system eliminates the need for users to manually search through numerous applications, directly resolving the time-loss problem while maintaining high relevance accuracy through data-driven selection
Solution Approach 2:
The patent introduces an intermediary recommendation system that acts as a mediator between the digital distribution platform and the user. This intermediary service analyzes crowd-sourced usage data and provides filtered, location-specific application recommendations, thereby reducing the information overload users face and eliminating the time-consuming manual search process while preserving application relevance
2Ease of operation
If the system provides a single most relevant application recommendation, then the user experience is improved by avoiding time-consuming searches, but the system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The system employs self-service mechanisms where mobile devices automatically contribute their application usage data to the crowd-sourced database without requiring user intervention. The network service then automatically processes this data to generate recommendations, eliminating the need for manual system configuration or complex user setup while improving ease of operation through automated, location-based application suggestions
Solution Approach 2:
The patent creates a universal recommendation system that serves multiple functions: it collects usage data, analyzes patterns across different locations, identifies the most relevant applications, and delivers recommendations to various users. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified service, managing complexity while enhancing user experience through comprehensive location-based recommendations
Data Source
AI summary
Crowd-sourced localized application usage data is collected from mobile devices at a usage location and sent to a network-based service. The network-based service analyzes the data to determine a single most relevant application correlated to the usage location. Once the most relevant application is determined, a recommendation for the application is sent to client devices operating at the usage location. In some implementations the data is processed to determine whether the usage location is a chained venue, a large venue or an event. Once the usage location has been determined, the most relevant application can be selected for recommendation.