Electronic Device Content Recommendation via Viewing History Analysis
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Solution Overview
Problem
Conventional methods for recommending content to users are complex and not widely used, as they require specific menu selections and multiple settings, leading to low usage frequency and user dissatisfaction.
Innovation Solution
An electronic device that utilizes a minimal resource-based approach to provide content recommendations by analyzing user history data, applying weights to viewing patterns, and calculating recommendation hit ratios to automatically suggest content based on user behavior, such as frequent viewing times and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional content recommendation methods (history service, bookmark, reservation) are used, then content recommendation functionality is provided, but user operation complexity increases and usage frequency decreases
Solution Approach 1:
The system automatically analyzes user viewing history and generates content recommendations without requiring user intervention. The processor autonomously identifies frequently viewed content and presents recommendations, eliminating the need for users to manually access history services or set bookmarks.
Solution Approach 2:
The system pre-analyzes user viewing patterns and prepares content recommendations in advance based on historical data. By continuously monitoring and analyzing viewing history, the system has recommendations ready before the user needs them, reducing operational complexity.
2Measurement precision
If manual menu selection and multiple setting operations are required, then content recommendation accuracy can be improved, but operation time and user effort increase
Solution Approach 1:
The system automatically collects and analyzes viewing history data without requiring user input or configuration. The processor autonomously determines user preferences by analyzing viewed content, eliminating the time users would spend on manual settings while maintaining recommendation accuracy.
Solution Approach 2:
The system continuously monitors user viewing behavior and uses this feedback to refine content recommendations. By analyzing actual viewing patterns and preferences, the system improves recommendation accuracy dynamically without requiring explicit user feedback or manual configuration.
3Ease of operation
If automated content recommendation is implemented, then user convenience is improved, but device resource consumption increases
Solution Approach 1:
The system implements automated content recommendation selectively rather than continuously. By triggering recommendations based on specific conditions (such as detecting when a user finishes viewing content or at predetermined intervals), the system provides user convenience while minimizing unnecessary processing and resource consumption.
Data Source
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AI summary
A method for recommending a content by an electronic device is disclosed. The method for recommending a content by an electronic device includes the steps of recommending a content on the basis of a viewing history, calculating recommendation hit ratios of the recommended content according to days of the week and times of the day on the basis of the selection frequency of the recommended content, and storing the same, and based on a specific event occurring, calculating a recommendation hit ratio of a content corresponding to the day and time when the specific event occurred, and based on the calculated recommendation hit ratio satisfying a predetermined condition, directly providing the content.