Split-Screen Application Recommendations Based on User Behavior
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Solution Overview
Problem
Current split-screen functions on electronic devices are not intelligent enough, making it complex for users to find applications for screen splitting, affecting user experience.
Innovation Solution
An application recommendation method that establishes an association relationship between applications based on user behavior data, providing a split-screen recommendation interface with recommended applications tailored to the user's habits and current scenario, facilitating quick selection of split-screen applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a traditional split-screen function is provided, then the basic screen splitting capability is achieved, but the operation complexity increases and user convenience deteriorates
Solution Approach 1:
The system performs preliminary action by analyzing user behavior data in advance to generate split-screen pair information and recommend applications before the user actually needs to split the screen. The electronic device proactively determines recommended applications based on historical usage patterns, so when the user initiates split-screen mode, the appropriate applications are already prepared and presented, eliminating the need for users to search through all applications.
Solution Approach 2:
The system implements self-service by automatically analyzing user behavior data and generating personalized split-screen recommendations without requiring user intervention. The electronic device autonomously processes usage history, identifies patterns, and presents relevant application pairs, allowing the system to serve itself in understanding and anticipating user needs rather than requiring manual configuration or search by the user.
2Ease of operation
If all applications supporting screen splitting are displayed, then the user has complete options, but the user experience deteriorates due to difficulty in finding the desired application
Solution Approach 1:
The system extracts and separates the most relevant applications from the complete set of applications supporting screen splitting. Based on user behavior data analysis, the electronic device identifies and extracts only the top recommended applications that are most likely to be needed, presenting just these selected applications in the split-screen recommendation interface rather than displaying all possible applications, thereby making the desired application easier to find.
Solution Approach 2:
The system applies local quality by providing different levels of application presentation tailored to user needs. The recommended application bar displays a curated selection of highly relevant applications based on user behavior patterns, while the complete application list remains available in the bar of applications supporting screen splitting. This creates a differentiated presentation where the most important applications are prominently featured with higher visibility and priority.
Data Source
AI summary
An application recommendation method and an electronic device. When the user triggers screen splitting, an electronic device may recommend, to the user based on the split-screen pair information specific for the user, one or more applications related to a foreground application, and display the recommended application on a split-screen recommendation interface for the user to select.