Trending Application Indexing via Installation Acceleration
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Solution Overview
Problem
Users of mobile computing devices face difficulties in discovering newly released and relatively unknown applications that are becoming popular within the application marketplace.
Innovation Solution
A method is implemented to identify trending applications by calculating a trending score based on the acceleration and fractional volume of installations, where acceleration is defined as the increase or decrease in the rate of installations over time, and fractional volume is the percentage of installations compared to all applications in the marketplace, with filtering applied to exclude certain types of content or applications with low installation rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users browse through the application marketplace to discover new applications, then users can find available applications, but it becomes difficult to discover newly released and relatively unknown applications that are becoming popular
Solution Approach 1:
The system pre-calculates and stores trending scores for applications based on installation data before users need to discover them. Installation histories are continuously monitored and processed to identify trending applications in advance, so when users access the marketplace, the trending information is already prepared and immediately available for display.
Solution Approach 2:
The patent introduces an intermediary trending score calculation system that mediates between raw installation data and user application discovery. This intermediary layer processes installation histories, applies scoring algorithms, and generates a curated list of trending applications, bridging the gap between unprocessed data and user-friendly presentation.
2Measurement precision
If the system calculates trending scores for all applications based on installation data, then accurate trending information is obtained, but the computational complexity and processing time increase
Solution Approach 1:
The system applies different weighting factors to different time periods in the installation history, giving more weight to recent installations and less weight to older ones. This local differentiation in the calculation approach allows the system to capture current trends accurately without being unduly influenced by historical data, improving precision while managing computational load.
Solution Approach 2:
The patent dynamically adjusts parameters such as the time window for calculating trending scores and the weighting factors applied to different installation periods. By changing these parameters based on market conditions and data availability, the system optimizes the balance between measurement precision and computational complexity.
3Loss of information
If the system processes and analyzes installation data for all applications, then comprehensive trending information is generated, but the time and computational resources required increase significantly
Solution Approach 1:
The system extracts only the essential features from installation data that are relevant for trending analysis, such as installation counts over time periods and rate of change metrics. By taking out only the necessary information rather than processing all raw data, the system reduces computational overhead while maintaining the completeness of trending information.
Solution Approach 2:
The patent processes installation data at different levels of detail for different applications based on their popularity and relevance. For highly popular applications, more detailed analysis is performed, while for less popular ones, simplified metrics are used. This partial action approach ensures comprehensive trending information for important applications while reducing overall processing time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving installation data, the installation data corresponding to one or more applications that can be installed and executed on mobile computing devices, receiving metadata corresponding to each of the one or more applications, for each application: generating a time series based on a number of installs, processing the time series and the metadata, calculating a score, determining that the score is greater than or equal to a threshold score, and in response to the determining, adding the respective application to an index of trending applications, storing the index of trending applications in computer-readable memory, retrieving the index of trending applications, and transmitting indications of one or more applications for display based upon the index of trending applications.