Closed Caption Analysis for Personalized Content Recommendations
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Solution Overview
Problem
Content delivery systems face challenges in providing personalized content recommendations to users due to the vast amount of content available, as existing methods often rely on predefined genres or titles, which can result in inaccurate recommendations and fail to account for the complexities of user preferences.
Innovation Solution
The system analyzes closed caption data to generate language-level metrics, such as reading level and sentence length, and updates these metrics based on user interactions, allowing for personalized content recommendations that align more closely with user interests by identifying linguistic characteristics and intended audiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If content recommendations are based on predefined genres or titles, then the recommendation system is simple to implement, but the accuracy of recommendations deteriorates
Solution Approach 1:
The patent transforms the recommendation approach from using predefined categorical parameters (genres, titles) to using dynamically extracted linguistic parameters from closed caption data. By analyzing sentence length, reading level, word frequency, and other language characteristics, the system creates nuanced content profiles that accurately reflect user preferences without relying on rigid predefined categories.
Solution Approach 2:
The patent introduces closed caption data as an intermediary layer between the content and the recommendation engine. Instead of directly using metadata like genres and titles, the system extracts linguistic features from caption text to create a more accurate representation of content characteristics, serving as a mediator that bridges content and user preferences.
2Measurement precision
If the system analyzes detailed caption data to generate language-level metrics, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system employs automated natural language processing algorithms that self-analyze closed caption data without requiring manual intervention. The linguistic feature extraction process is performed automatically through computational algorithms that calculate metrics such as reading level, sentence complexity, and word frequency, eliminating the need for manual content analysis while maintaining high precision.
Solution Approach 2:
The patent replaces manual or mechanical content analysis methods with automated computational language processing. Instead of human reviewers or simple keyword matching, the system uses algorithms to automatically extract and analyze linguistic characteristics from caption data, substituting mechanical processes with intelligent automated systems that handle complexity internally.
3Adaptability or versatility
If the system uses user interaction data to update metrics continuously, then personalization improves, but data processing requirements increase
Solution Approach 1:
The system implements a feedback loop where user interactions with recommended content continuously update the linguistic metrics and user profiles. As users view or interact with content, the system analyzes the caption data of that content and adjusts future recommendations accordingly, creating a self-improving personalized recommendation system that adapts to changing user preferences over time.
Solution Approach 2:
The system processes caption data selectively rather than analyzing every piece of content uniformly. By focusing computational resources on analyzing caption data for content that users actually interact with or that is most relevant to their demonstrated preferences, the system achieves effective personalization while managing data processing volume through targeted rather than exhaustive analysis.
Data Source
AI summary
Systems, apparatuses, and methods are described herein for providing language-level content recommendations to users based on an analysis of closed captions of content viewed by the users and other data. Language-level analysis of content viewed by a user may be performed to generate metrics that are associated with the user. The metrics may be used to provide recommendations for content, which may include advertising, that is closely aligned with the user's interests.