Video Demographics Analysis Using Content-Based Classifier Models
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Solution Overview
Problem
Conventional systems for video hosting platforms rely on external metadata, which can be inaccurate, to suggest videos to users, failing to effectively predict demographic interests based on actual viewing habits and content analysis.
Innovation Solution
A video demographics analysis system that creates classifier models to predict demographic characteristics of videos and users by analyzing features from viewed content, user demographics, and viewing patterns, enabling more accurate recommendations and content targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems rely on external metadata (keywords, textual descriptions) to predict demographic groups, then the system is simpler to operate and requires less processing, but the accuracy of demographic prediction deteriorates because metadata can be false or misleading
Solution Approach 1:
The patent replaces the mechanical/extraction-based approach (relying on external metadata keywords and textual descriptions) with an automated content analysis system that uses computer vision, audio processing, and machine learning to directly analyze video content and infer demographic characteristics. This substitution eliminates the need for manual metadata curation while significantly improving prediction accuracy by examining actual video content rather than relying on potentially misleading metadata.
2Device complexity
If conventional systems use simple keyword matching for video recommendations, then the device complexity is reduced, but the reliability of recommendations deteriorates when metadata is inaccurate or spammy
Solution Approach 1:
The patent segments the video analysis process into multiple independent modules: visual content analysis, audio content analysis, metadata analysis, and demographic inference. Each module processes specific aspects of the video separately using specialized algorithms (e.g., frame-based visual recognition, audio spectrum analysis), and the results are integrated to produce comprehensive demographic predictions. This segmentation allows the system to handle complex analysis tasks while maintaining modularity and manageability.
Solution Approach 2:
The patent creates a composite recommendation system that integrates multiple data sources and analysis methods: visual features from video frames, audio features from soundtracks, textual metadata, and user behavior patterns. By combining these diverse data types through a unified machine learning model, the system achieves high reliability in demographic prediction while managing complexity through structured integration of multiple analysis components.
3Measurement precision
If the system analyzes actual video content features (audiovisual content, viewing sessions) to predict demographics, then the accuracy of demographic attributes improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing video content features during video upload and initial processing. Visual frames are extracted and indexed, audio tracks are analyzed and tagged with temporal markers, and metadata is structured in advance. This preliminary preparation creates ready-to-query data structures that enable rapid demographic inference during actual recommendation operations, significantly reducing real-time processing requirements while maintaining high accuracy.
Data Source
AI summary
A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and/or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and/or distributions of viewings over hours of the day, days of the week, and the like.