Device Context Aware Application Recommendation System
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Solution Overview
Problem
Current recommendation systems for applications do not consider the device environment or characteristics where the application will be executed, leading to suboptimal performance and user satisfaction.
Innovation Solution
An interactive computing system that collects performance data from user devices to recommend alternative applications or device modifications that improve application performance, using algorithms to compare expected performance data with actual performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems use traditional behavior-based associations (purchase history, viewing history) to generate recommendations, then they can provide personalized recommendations, but they fail to consider device environment characteristics leading to suboptimal application performance
Solution Approach 1:
The patent extends the recommendation system from traditional user behavior dimensions (purchase history, viewing history) to include a new dimension: device environment characteristics. By collecting and analyzing device metrics (battery level, memory usage, processor speed, screen resolution, operating system version), the system creates a multi-dimensional recommendation space that simultaneously considers user preferences and device capabilities, resolving the contradiction between personalization and performance reliability
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on real-time device state. Instead of using static user profiles alone, the recommendation algorithm incorporates variable device parameters (battery level, available memory, processor capacity) to modify recommendation outcomes. This allows the same user to receive different recommendations depending on their current device conditions, ensuring both personalization and performance reliability
2Measurement precision
If the system collects and analyzes performance data from multiple devices to improve recommendation accuracy, then recommendation quality improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent creates simplified copies of device characteristics through standardized device profiles. Instead of processing raw, complex device data in its original form, the system generates abstracted device profiles that capture essential performance characteristics in a standardized format. These profiles serve as manageable representations that preserve measurement precision while reducing system complexity and facilitating efficient comparison across multiple devices
Solution Approach 2:
The system implements a universal device profile structure that can represent multiple device types and characteristics through a common framework. This multi-functional profile system handles diverse device data (mobile phones, tablets, computers with varying specifications) using unified data structures and processing logic, enabling accurate cross-device recommendations without proportionally increasing system complexity
3Productivity
If the recommendation system only considers user behavior history without device context, then the system remains simple and fast, but it cannot recommend applications that are optimized for the user's specific device
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing device characteristic data in device profiles before recommendations are needed. Device metrics such as processor type, memory capacity, battery level, and screen resolution are gathered and cached in advance. When a recommendation request occurs, the system retrieves pre-stored device profiles rather than collecting data in real-time, maintaining fast processing speed while enabling device-specific optimization
Data Source
AI summary
Systems and associated processes are disclosed for generating recommendations for users based on the computing device likely to be utilized by the user to execute an application, 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 or for other computing devices. The performance of applications running on the user's computing device can be monitored with the performance data being collected and provided to the interactive computing system. The interactive computing system can include a recommendation system or service that processes the performance data and using the performance data, among possibly other data, the recommendation system can recommend alternative applications to the user for download. Further, in some cases, the interactive computing system can recommend modifications to the user's computing device to improve the performance of the application running on the user's computing device.