Context-Aware Media Ranking for Portable Devices
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Solution Overview
Problem
Conventional portable electronic devices do not effectively utilize context information and user behavior to recommend the most important items, such as photos or videos, on their user interfaces.
Innovation Solution
A content recommendation method and device that fetches current context information, calculates a relevant ranking value for each media item based on this information, sorts these values, and displays highlighted items on the user interface, incorporating user interactions and preferences to prioritize important content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional portable electronic devices display all media files on the user interface, then users can access all their media files, but the most important items (photos or videos) are not highlighted or prioritized
Solution Approach 1:
The patent extracts the most important media items from the complete set of media files by calculating relevance scores based on context information (location, time, user behavior) and displays only these highlighted items on the user interface. This separates the critical information (important items) from the non-critical information (other media files), resolving the contradiction by presenting only the essential content while maintaining accessibility to the full library through additional user actions.
2Measurement precision
If the device uses context information and user behavior to calculate relevance rankings, then the most important items can be recommended, but the system complexity increases
Solution Approach 1:
The patent leverages existing multi-functional components in the portable electronic device (GPS for location, accelerometer for motion detection, touch screen for user interaction tracking) to gather context information. By reusing these existing sensors and data collection mechanisms for the additional purpose of calculating media item relevance, the system achieves precise ranking without proportionally increasing hardware complexity.
Solution Approach 2:
The patent transforms multiple context parameters (location coordinates, time stamps, user interaction frequency, motion data) into a single relevance score parameter through weighted calculation. This parameter transformation approach allows the system to process complex multi-dimensional context information while maintaining a simplified output structure that can be easily displayed and managed on the user interface.
Data Source
AI summary
A content recommendation method for use in a portable electronic device is provided. The method includes the steps of fetching current context information from the portable electronic device; calculating a relevant ranking value of each item within each type of media files stored in the portable electronic device based on the context information; sorting the relevant ranking value of each item within each type of the media files; highlighting at least one of the items of a first user interface of the portable electronic device according to the sorted ranking values.