Content Presentation System Using Symbolic Relevance Analysis
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Solution Overview
Problem
Users face difficulties in finding content that matches their current preferences due to the limitations of existing search and recommendation systems, which require inputting search conditions and rely on historical viewing data that may not accurately reflect changing tastes.
Innovation Solution
A content presentation system with a server and connected devices that analyze user attributes and content symbols to provide relevance information, allowing users to find content without manual search inputs by reflecting the actual viewing habits of similar users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a search function is provided to find content based on user input, then content search capability is improved, but operation complexity increases and user convenience deteriorates
Solution Approach 1:
The system automatically performs content analysis and recommendation without requiring user input. The content reproduction device autonomously analyzes reproduction history, extracts symbols, and generates recommendation lists, allowing the system to serve itself rather than requiring active user participation in the search process.
Solution Approach 2:
The system performs content analysis and symbol extraction in advance, before the user needs to search for content. By pre-processing the content library and organizing it into symbolic representations, the system prepares recommendation data proactively, so when the user views the recommendation list, the work is already done.
2Adaptability or versatility
If recommendation function uses historical content reproduction data, then personalization is improved, but accuracy deteriorates due to changing user tastes
Solution Approach 1:
The system changes the parameters used for recommendation from direct historical content data to extracted symbolic representations. By transforming content into symbols and analyzing symbol frequencies and patterns, the system captures user taste changes more dynamically, allowing recommendations to adapt to evolving preferences rather than being constrained by static historical data.
Solution Approach 2:
The system replaces the mechanical approach of directly analyzing historical content reproduction data with a symbolic processing mechanism. Instead of mechanically reviewing past viewing records, the system uses symbol extraction and statistical analysis to infer user preferences, creating a more flexible and accurate representation of user taste that can adapt to changes over time.
3Quantity of substance
If vast amount of content is stored in content reproduction device, then content availability is improved, but content extraction difficulty increases
Solution Approach 1:
The system extracts essential symbolic representations from the vast content library, separating the core meaningful elements (symbols) from the bulk content data. By taking out only the symbolic features that represent content essence, the system creates a compact representation that is easy to search and analyze, solving the problem of extracting desired content from large volumes.
Solution Approach 2:
The system segments the vast content library into discrete symbolic units, organizing content by symbols rather than treating it as a monolithic mass. This segmentation allows the system to efficiently analyze and search through content by working with individual symbols and their frequencies, making content extraction manageable even from very large content collections.
Data Source
AI summary
To make it possible for a user to smoothly find content which satisfies the user's current taste without the need to carry out a troublesome input operation. Each of the content reproduction devices reproduces a content selected from among one or more reproduction object contents according to designation by the user. The content presentation assistance server analyzes, for every user attribute, relevance of one or more symbols relevant to each of the contents reproduced according to the designation of the user having that user attribute, and sends an analyzed result to the content reproduction used by the user having user attribute relevant to that analyzed result. Each of the content reproduction devices acquires, for the content, one or more relevant symbols, and displays content relevance information which describes the relevance of the reproduction object contents based on the symbols and the analyzed result.