Character Vector Models for Media Recommendation Precision
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Solution Overview
Problem
Consumers face inefficiencies in discovering new media content that matches their personal preferences due to traditional techniques relying on narrow precision and unreliable methods, such as friend recommendations or previews, which fail to accurately identify character qualities across different roles played by actors.
Innovation Solution
The development of character models in vector space, which represent attributes of characters in media content, allows for efficient and reliable decomposition of character attributes, enabling precise searches and recommendations based on user preferences through character preference functions and salience values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques (friend recommendations, previews) are used for media discovery, then the process is simple and familiar, but the precision and reliability of identifying content that matches user preferences deteriorates
Solution Approach 1:
The patent segments character representation into discrete attributes (e.g., intelligence, kindness, aggression) that can be independently measured and compared. Each character is decomposed into a vector of attribute values, allowing precise identification of character qualities without requiring complex holistic analysis.
Solution Approach 2:
The patent transforms qualitative character descriptions into quantitative parameter representations. By mapping character attributes to numerical values and using vector space models, the system enables precise measurement and comparison of character qualities through mathematical operations rather than subjective judgment.
2Reliability
If character attributes are decomposed into multiple dimensions for precise matching, then the accuracy of media recommendations improves, but the complexity of processing and storing character models increases
Solution Approach 1:
The patent replaces complex qualitative analysis and subjective recommendation processes with mathematical vector operations. Character matching is achieved through efficient vector similarity calculations (e.g., cosine similarity) rather than manual comparison of narrative descriptions, significantly improving reliability while reducing processing complexity.
Solution Approach 2:
The patent creates a universal character model framework that can represent any character across different media using the same attribute dimensions and vector space. This multi-functional approach allows the same processing mechanisms to handle diverse characters and media types, improving recommendation reliability without proportionally increasing system complexity.
3Productivity
If comprehensive character attribute analysis is performed across multiple media contents, then the ability to discover matching content improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary decomposition of character attributes and pre-computation of vector representations for all characters in the media database. By preparing character models in advance with standardized attribute vectors, the system enables rapid similarity searches without performing comprehensive analysis during the actual discovery process, significantly reducing user wait time.
Solution Approach 2:
The patent creates simplified vector copies of character representations that capture essential attributes without containing all the detailed narrative information. These compact vector copies enable efficient comparison and matching operations, allowing comprehensive character analysis to be performed quickly without processing the full complexity of original media content.
Data Source
AI summary
Techniques for recommending media are described. A character preference function comprising a plurality of preference coefficients is accessed. A first character model comprises a first set of attribute values for the plurality of attributes of a first character. The first and second characters are associated with a first and second salience value, respectively. A second character model comprises a second set of attribute values for the plurality of attributes of a second character of the plurality of characters. A first character rating is calculated using the plurality of preference coefficients and the first set of attribute values. A second character rating of the second character is calculated using the plurality of preference coefficients with the second set of attribute values. A media rating is calculated based on the first and second salience values and the first and second character ratings. A media is recommended based on the media rating.