Usage-Based Recommendation Engine for Mobile Apps
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Solution Overview
Problem
Current recommendation systems do not consider actual usage patterns of items by users when generating personalized recommendations, as they typically lack data on how often, when, and if an item is used after purchase.
Innovation Solution
An interactive computing system that collects and analyzes usage data from mobile devices and other computing systems to detect usage patterns, enabling the generation of usage-based recommendations by tracking application usage metrics such as frequency, location, and duration, and combining this data with social and behavior-based algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems use only purchase history and item viewing history to generate recommendations, then the system complexity remains low, but the recommendation accuracy and relevance are insufficient because actual usage patterns are not captured
Solution Approach 1:
The patent segments the data collection process into multiple components: purchase history tracking, item viewing history tracking, and actual usage pattern tracking. Each segment collects specific types of data independently, which are then integrated to form a comprehensive user profile for more accurate recommendations without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary data collection mechanism that captures actual usage patterns between purchase and recommendation generation. This intermediary layer collects usage data from various sources (mobile devices, computing systems) and processes it into actionable insights, bridging the gap between simple purchase history and complex recommendation accuracy requirements
2Reliability
If the system collects comprehensive usage data from mobile devices and computing systems, then the relevance of recommendations improves, but the data processing complexity and resource requirements increase
Solution Approach 1:
The patent implements preliminary data processing by collecting and organizing usage data from mobile devices and computing systems before it reaches the main recommendation engine. Data is pre-filtered, aggregated, and structured in advance, reducing the processing burden on the core system while maintaining high recommendation relevance
Solution Approach 2:
The system enables self-service data collection where mobile devices and computing systems automatically report their own usage patterns without requiring intensive centralized processing. Each device serves itself by collecting local usage data and transmitting only essential information to the recommendation system, reducing overall data processing complexity
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.