Usage Pattern Analysis for Mobile App Recommendations
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Solution Overview
Problem
Current recommendation systems do not account for actual usage patterns of items by users, relying solely on purchase and selection history without considering how often or when items are used, leading to less effective personalized recommendations.
Innovation Solution
An interactive computing system that collects and analyzes usage data from mobile devices and other computing systems to detect usage patterns, generating recommendations based on how applications or items are used, including frequency, location, and time of use, and combining this data with social network information to provide personalized suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems rely solely on purchase and selection history, then the system complexity remains low, but the recommendation accuracy and relevance deteriorate
Solution Approach 1:
The patent combines multiple data sources including purchase history, item viewing history, and usage data into a unified recommendation framework. By merging these different types of information, the system achieves more accurate recommendations while managing complexity through integrated data processing.
Solution Approach 2:
The patent introduces usage data as an intermediary element that bridges the gap between simple purchase/selection records and complex behavioral patterns. This intermediary data layer enables more precise recommendations without directly increasing system complexity, as it serves as a mediator between basic transaction data and advanced recommendation algorithms.
2Loss of information
If the system collects and analyzes usage data from multiple sources, then recommendation relevance improves, but data processing complexity increases
Solution Approach 1:
The patent segments usage data into distinct categories such as frequency of use, time of use, and location of use. This segmentation allows the system to process and analyze different aspects of usage behavior separately, reducing overall data processing complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The patent adds new dimensions to the recommendation system by incorporating temporal (time of use) and spatial (location of use) variables alongside traditional purchase and viewing data. This dimensional expansion enriches the information base without proportionally increasing processing complexity, as the additional dimensions are integrated into existing analytical frameworks.
Data Source
AI summary
This disclosure describes systems and associated processes for generating recommendations for users based on usage, among other things. These systems and processes are described in the context of an interactive computing system that enables users to download applications for mobile devices (such as phones) or for other computing devices. Users' interactions with applications once they are downloaded can be observed and tracked, with such usage data being collected and provided to the interactive computing system. The interactive computing system can include a recommendation system or service that processes the usage data from a plurality of users to detect usage patterns. Using these usage patterns, among possibly other data, the recommendation system can recommend applications to users for download.