ML Model for Personalized Multimedia Segment Streaming
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Solution Overview
Problem
Current content delivery systems lack the ability to dynamically segment multimedia files based on viewer interest without manual intervention, failing to provide personalized experiences efficiently.
Innovation Solution
A machine learning model is employed to identify and generate a proper subset of segments from multimedia files using annotation services like music detection, object detection, and scene change detection, allowing for personalized content delivery without requiring viewers to actively control the media player timeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation of multimedia files is performed to provide personalized content, then content delivery accuracy improves, but labor cost and time consumption increase significantly
Solution Approach 1:
The system enables automatic self-segmentation of multimedia files through machine learning models that autonomously analyze content, detect scene changes, and generate personalized segments without human intervention. The model processes viewer preferences and automatically delivers customized content subsets, eliminating manual labor while maintaining high accuracy.
2Reliability
If complete multimedia files are delivered to all viewers, then content completeness is ensured, but network bandwidth consumption and storage requirements increase
Solution Approach 1:
The system extracts and delivers only the relevant segments of multimedia files that match viewer preferences. The machine learning model identifies and separates interesting portions from the complete file, transmitting only these extracted segments to individual viewers. This maintains content reliability for each user while significantly reducing overall network bandwidth consumption and storage requirements.
3Device complexity
If traditional content delivery systems are used without personalization, then system complexity remains low, but viewer satisfaction and engagement decrease
Solution Approach 1:
The system segments both the multimedia content and the viewer base into distinct groups based on preferences and behaviors. By dividing content into manageable segments and matching them to specific viewer profiles, the system achieves high adaptability and personalization capability while keeping the underlying architecture relatively simple and scalable.
Data Source
AI summary
Techniques for using a machine learning model to determine a proper subset of a multimedia file for a viewer based on their interest without the need to actively control a media player timeline are described. As one example, a computer-implemented method includes receiving a request at a content delivery service from a media player of a viewer to play a proper subset of a live multimedia file for a category of content of the live multimedia file without the viewer actively controlling a timeline of the media player of the viewer, determining an indication of a prior multimedia playing interaction of the viewer with the content delivery service, partitioning, by the content delivery service, the live multimedia file into a video portion, an audio portion, and a text portion, determining, by the content delivery service, one or more labels for the video portion, the audio portion, and the text portion, determining, by a machine learning model of the content delivery service, a proper subset of segments of the live multimedia file to send to the viewer based at least in part on the indication and the one or more labels, and live streaming the proper subset of segments of the live multimedia file to the media player of the viewer.