Dynamic Content Search Control Using Interest Vectors
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Solution Overview
Problem
Existing content search systems struggle to accurately reflect changes in user interest and expand the target range of content preferences in automatic content selection, leading to increased system load and difficulty in updating search conditions based on user behavior.
Innovation Solution
A content information search control system that uses coordinate axes to store user position information and content attributes, allowing for the generation of interest vectors and distance information to dynamically update search conditions, thereby adjusting the search range and user position to better match evolving user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If massive history information is stored to meet various user preferences, then content selection accuracy is improved, but system load is increased
Solution Approach 1:
The patent extracts only the essential elements needed for content selection (user position on coordinate axes, interest vectors, and recent access patterns) while discarding the bulk of historical data. This allows accurate content recommendation without storing massive history information, thereby reducing system load while maintaining selection accuracy.
Solution Approach 2:
The system pre-calculates and stores user position information on coordinate axes and interest vectors in advance, based on recent access history. This preliminary processing enables fast content selection without needing to analyze entire history databases, reducing both storage requirements and system load during actual content recommendation.
2Adaptability or versatility
If search conditions are updated to reflect user interest changes, then adaptability is improved, but device complexity is increased
Solution Approach 1:
The patent implements dynamic search condition updates by continuously monitoring user access patterns and automatically adjusting user position on coordinate axes and interest vectors. The system adapts to changing user preferences in real-time without requiring complex manual configuration, achieving high adaptability through automated dynamic adjustments.
Solution Approach 2:
The system uses feedback from user access history to automatically update search conditions. By analyzing recent access patterns and calculating interest vectors, the system continuously refines user position information and adjusts content recommendations, creating a self-adapting mechanism that reduces complexity through automated feedback loops.
3Adaptability or versatility
If the target range of content is expanded, then user preference coverage is improved, but measurement precision is reduced
Solution Approach 1:
The patent applies local quality by adjusting the search radius dynamically based on user position and interest vector strength. For users with strong, focused interests, the search concentrates in specific regions of the coordinate space with high precision. For users with broader interests, the search naturally expands to cover wider areas, achieving both precision and coverage through localized search strategies.
Solution Approach 2:
The system uses multi-dimensional coordinate axes to represent user preferences, allowing the search to expand into additional dimensions rather than simply increasing search breadth in a single dimension. This enables the system to cover a wider target range while maintaining precision by navigating through multiple preference dimensions (e.g., genre, difficulty, platform) simultaneously.
Data Source
AI summary
Disclosed is a contents information search control system that, when automatic search of contents, such as game software, is performed with a terminal, such as a cellular phone, according to a user's preference, can reflect a change in user's interest or expansion of a target range of an interest. A change in user's interest is quantitatively grasped as an interest vector, and when the interest vector satisfies a predetermined condition, a search condition is updated to reflect the change in the user's interest in a search condition. In addition, expansion of a target range of the user's interest is quantitatively grasped as distance information, and when the distance information satisfies a predetermined condition, the search condition is updated to reflect the expansion of the target range of the user's interest in the search condition. The interest vector or the distance information is calculated from an automatic search result on the basis of contents selected by a user, and updated as occasion demands.