Media File Scoring for Privacy-Aware Content Recommendations
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Solution Overview
Problem
Existing media content recommendation systems rely heavily on intrusive data collection to provide recommendations, lacking nuanced insights into how media content represents diverse demographics and user preferences, leading to inefficient engagement and potential privacy concerns.
Innovation Solution
A system that parses media files using factors of interest, determines a factor score, and performs scored actions to provide targeted recommendations, reducing data collection and processing time while enhancing user engagement and content representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large scale data collection is used to provide recommendations, then recommendation accuracy is improved, but user privacy is compromised and system complexity increases
Solution Approach 1:
The patent extracts only the essential scoring elements needed for media evaluation (visual quality, audio quality, content accuracy) rather than collecting comprehensive user behavior data. This selective extraction approach maintains recommendation accuracy while minimizing privacy intrusion by focusing solely on objective media file characteristics.
Solution Approach 2:
The patent introduces an intermediary scoring system that objectively evaluates media files against reference data without requiring direct access to user personal information. This intermediary layer mediates between the recommendation system and user data, providing accurate recommendations while preserving privacy through objective, data-minimal evaluation.
2Measurement precision
If comprehensive data collection is performed to understand consumer preferences, then content recommendation quality is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary scoring of media files against reference data in advance, storing the results for quick retrieval. This preliminary action eliminates the need for real-time processing of large datasets when generating recommendations, significantly reducing data processing time while maintaining recommendation quality.
Solution Approach 2:
The patent extracts only the critical scoring elements (visual quality score, audio quality score, content accuracy score) from media files rather than processing all available data. This selective extraction reduces the volume of data requiring processing while preserving the essential information needed for high-quality recommendations.
3Measurement precision
If objective scoring methods are used to evaluate media content, then representation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the media evaluation process into distinct, independent scoring components: visual quality assessment, audio quality assessment, and content accuracy assessment. Each component evaluates a specific aspect against reference data, improving representation accuracy through focused measurement while reducing system complexity by breaking down the overall evaluation into manageable, modular segments.
Data Source
AI summary
Embodiments provide for parsing a media file using a factor of interest, determining a factor score for the media file, and performing a scored action based on the factor score to provide a media content recommendation to a user/consumer or to content providers. The scored action may include sorting and filtering a media repository, including the media file, which in turn reduces an amount of data needed for a system to provide an objective recommendation to a user, as well as reducing the time and data processing required to provide a recommendation to the user.