Content Recommendation Device Using Image Audio User Recognition
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Solution Overview
Problem
Current content recommendation systems rely on user metadata and manual ID inputs, lacking the ability to automatically recognize users and provide personalized content recommendations without registration, and struggle to handle situations like new or absent users effectively.
Innovation Solution
A method and device that extract user features from image and audio data to determine recognition rates, allowing for automatic user modeling and content recommendation based on thresholds, and can detect new or absent users to provide tailored content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic user recognition based on image and audio data is implemented, then user personalization and ease of operation are improved, but device complexity and measurement precision requirements increase
Solution Approach 1:
The user recognition system is divided into separate modules: image data processing module, audio data processing module, and user model matching module. Each module handles specific tasks independently, reducing overall system complexity while enabling automatic recognition functionality.
Solution Approach 2:
A user model database serves as an intermediary layer between the input data (images/audio) and the content recommendation system. The system compares extracted user features against stored user models to identify users without requiring direct complex analysis, simplifying the recognition process.
2Measurement precision
If multiple user features are analyzed with recognition rates, then user recognition accuracy is improved, but measurement precision and processing time increase
Solution Approach 1:
The system calculates recognition rates for multiple user features (image, audio, and combinations) but uses threshold-based filtering to avoid processing all possibilities. When recognition rate exceeds a threshold, the system stops further analysis, reducing processing time while maintaining accuracy.
Solution Approach 2:
User models are pre-established with expected feature patterns before actual recognition occurs. This preliminary preparation allows the system to quickly compare incoming data against known patterns rather than analyzing all features from scratch, reducing real-time processing time.
3Adaptability or versatility
If the system handles new and absent users with content recommendations, then adaptability and user experience are improved, but device complexity and information processing requirements increase
Solution Approach 1:
When new or absent users are detected, the system automatically provides default content recommendations based on available data without requiring manual user input or registration. The system serves itself by generating recommendations even when complete user information is unavailable, improving adaptability while minimizing information loss.
Data Source
AI summary
A content recommendation method and device for recommending content to a user are disclosed. According to one embodiment, the content recommendation device extracts the features of a user from image data, audio data and the like, and can determine a recognition rate indicating the degree that is recognized as a user model predetermined according to the features of the user. The content recommendation device can determine the recommended content to be provided to the user on the basis of the determined recognition rate.