Mobile Application Recommendation System Using Time and Location Context
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Solution Overview
Problem
Users of mobile terminals face inconvenience due to the time and effort required to search for desired applications among numerous options, necessitating a method to recommend applications based on user intentions.
Innovation Solution
A method that involves a server receiving and classifying application usage frequencies by time and location, selecting applications based on the mobile terminal's current time and location information, and transmitting recommendation information to the terminal for display, allowing users to quickly access frequently used applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users search for applications manually among numerous options, then users can find desired applications, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by collecting usage frequency data and time-location information in advance, then pre-calculates and stores recommended application lists before users need them. When a user accesses the terminal, the pre-prepared recommendations are immediately displayed based on current time and location, eliminating the need for users to manually search through numerous applications.
2Adaptability or versatility
If the number of applications increases to meet user needs, then application functionality improves, but the complexity of selecting the right application increases
Solution Approach 1:
The system applies local quality by providing different application recommendations tailored to specific time periods and locations. Instead of presenting all available applications uniformly, the terminal analyzes current time and location context to display only the most relevant applications for that particular situation, making the selection process simpler and more targeted.
Solution Approach 2:
The system changes parameters by dynamically adjusting application recommendations based on varying time and location parameters. The recommendation list is not static but changes according to the current time of day, day of week, and geographical location, allowing the system to adapt to different user needs across different contexts while maintaining simple selection.
3Measurement precision
If the system recommends applications based on detailed time and location analysis, then recommendation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary data processing by pre-collecting and analyzing usage frequency data across different time periods and locations. This historical data is processed in advance to establish patterns and relationships, storing the results in a format that enables quick retrieval and comparison when generating real-time recommendations, thus reducing the processing burden at the moment of recommendation.
Data Source
AI summary
A method of recommending an application, which is capable of selecting and recommending an application with a high possibility of use, the method including: receiving, in a server, frequencies of use of a plurality of applications that are classified according to a time when each application is executed or a location where each application is executed; selecting an application from among the plurality of applications based on time and location information of where a mobile terminal is located and the frequency of use of the application; and transmitting application recommendation information including the selected application from the server to the mobile terminal.