Display Device Service Prediction Using Environmental Parameters
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Solution Overview
Problem
Existing display methods continuously recommend the same services to users based on usage frequency, leading to inaccurate recommendations due to varying user needs at different times.
Innovation Solution
A method that predicts services based on user habits by acquiring environmental parameters and displaying service information on a preset desktop page, avoiding frequency-based recommendations by actively suggesting services aligned with user habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If services are recommended based on usage frequency, then the system can provide personalized recommendations, but the recommendation accuracy deteriorates because users have different needs at different times
Solution Approach 1:
The patent changes the recommendation parameters from static usage frequency to dynamic environmental parameters (time, location, weather, etc.). The service recommendation system now considers multiple environmental parameters that change over time, allowing it to adapt recommendations to current conditions rather than relying solely on historical usage frequency data.
Solution Approach 2:
The patent introduces dynamic environmental parameters (time, location, weather conditions) that continuously change, making the recommendation system dynamic rather than static. The system now adapts recommendations based on real-time environmental changes and user behavior patterns across different contexts, rather than relying on fixed frequency-based rankings.
2Device complexity
If the system continuously recommends the same services based on high usage frequency, then implementation is simple, but the user experience deteriorates due to irrelevant recommendations
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and analyzing environmental parameters (time, location, weather, user habits) and pre-processing service data. The system establishes user habit profiles and environmental parameter associations in advance, so that when a recommendation is needed, it can quickly match current environmental conditions with pre-analyzed patterns rather than computing from scratch.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user interactions with recommended services and environmental parameters. This feedback is used to refine user habit profiles and improve the accuracy of environmental parameter-service associations over time, creating a self-improving recommendation system that adapts to changing user preferences and behaviors.
Data Source
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AI summary
The present disclosure relates to a method for operating a display device. The method includes that: a preset operation is received, the preset operation being configured to indicate a terminal to display a preset desktop page; a parameter of an environment where the terminal is located is acquired; service to be called by a user is predicted according to the parameter; and information of the service is displayed in the preset desktop page.