Split-Screen Display Application Recommendation via Clustering
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Solution Overview
Problem
Current split-screen display solutions for intelligent devices are inefficient and do not consider user scenarios well, leading to poor user experience due to complex and rigid operation processes.
Innovation Solution
A split-screen display processing method that uses a clustering algorithm to recommend a to-be-displayed application on a split-screen page, allowing users to easily select applications without searching through all installed applications, and allows for intuitive screen splitting operations such as touching and holding the edge or sliding towards the center of the touchscreen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually searches for and drags applications to enable split-screen display, then the split-screen function can be activated, but the operation process becomes complex and time-consuming
Solution Approach 1:
The system pre-calculates and stores the optimal pairing relationships between applications based on historical data and clustering algorithms. When split-screen is activated, the recommended application is immediately provided without requiring the user to search through all installed applications, thus performing the selection action in advance.
Solution Approach 2:
The system automatically recommends the most suitable application for split-screen based on predefined algorithms and historical usage patterns. The system serves itself by autonomously selecting the paired application without requiring manual user intervention to search and choose, thereby reducing operational complexity and time.
2Adaptability or versatility
If the system displays all installed applications for user selection, then the user can choose any application, but the interface becomes cluttered and selection becomes difficult
Solution Approach 1:
The system extracts only the most relevant application from the complete set of installed applications based on clustering analysis and historical data. Instead of displaying all applications, the system isolates and presents only the recommended application that best matches the current context, thereby simplifying the interface while maintaining versatility.
Solution Approach 2:
The system applies different presentation qualities to different applications based on their relevance. The recommended application receives prominent display with enhanced visibility, while other applications are either hidden or shown with reduced emphasis. This localized quality enhancement allows the interface to remain simple while still providing access to diverse application options.
3Reliability
If the system uses traditional split-screen activation methods, then the function can be enabled, but the process does not consider user scenarios and provides poor user experience
Solution Approach 1:
The system incorporates historical split-screen usage data and user behavior patterns into the recommendation algorithm. By continuously learning from past user decisions and application pairing preferences, the system provides increasingly accurate recommendations that adapt to individual user scenarios, thereby improving both reliability and ease of operation.
Solution Approach 2:
The system dynamically adjusts the recommendation parameters based on the current application context, user preferences, and historical data. Instead of using a fixed method for all scenarios, the system modifies its selection criteria according to the specific situation, such as changing the weight of different factors in the clustering algorithm based on the type of application being used.
Data Source
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Figure 4(a)~4(b)
AI summary
A split-screen display processing method and apparatus, a device, and a storage medium are provided. In this method, the device obtains a screen splitting operation performed by a user on a touchscreen (S101); splits a current display screen into two areas based on the screen splitting operation, and displays, in a first area, an interface of a first application that is displayed on the display screen before the screen splitting (S102); obtains, from installed applications by using a clustering algorithm, at least one second application to be combined with the first application in a split-screen manner (S103); and displays an identifier of the at least one second application in a second area after the screen splitting (S104). A to-be-displayed application is recommended on a split-screen page based on a specific algorithm, so that the user does not need to find an application that the user wants to start from all the applications, so as to improve split-screen display efficiency and facilitate an operation.