Audio Segment Recommendation via ML Topic Tagging
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Solution Overview
Problem
Existing technologies lack an efficient method to segment and recommend relevant audio segments from long-form audio content episodes, such as podcasts, which hinders user engagement and content discovery.
Innovation Solution
The method involves analyzing audio content episodes using machine learning models to identify audio segments based on characteristics like topics, speakers, duration, music, and advertisements, and then automatically tagging and recommending these segments to users through a segment feed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users consume entire audio content episodes, then they get complete information, but it consumes excessive time and reduces user engagement
Solution Approach 1:
The patent segments long-form audio content episodes into shorter, topic-based clips that users can consume individually. This segmentation allows users to get specific information without committing to listening to entire episodes, thus reducing time consumption while maintaining information accessibility.
Solution Approach 2:
The system extracts key segments and highlights from audio episodes based on topic analysis and user preferences. By taking out only the most relevant portions, the system enables users to consume condensed information quickly without losing essential content value.
2Loss of information
If the system recommends entire episodes, then users get comprehensive content, but it reduces content discovery efficiency and user engagement
Solution Approach 1:
The patent divides audio episodes into topic-based segments that can be independently recommended. This allows the system to present users with specific, relevant content snippets rather than entire episodes, improving discovery efficiency while maintaining content quality.
Solution Approach 2:
The system applies local quality by tailoring recommendations to individual user preferences and listening habits. By analyzing user behavior and preferences, the system curates segments with locally optimized quality that matches each user's specific interests, enhancing both discovery efficiency and engagement.
3Ease of operation
If the system provides personalized recommendations, then user engagement improves, but the system complexity increases
Solution Approach 1:
The patent implements self-service by automatically analyzing user listening behavior, preferences, and patterns to generate personalized recommendations without requiring manual user input. The system serves itself by continuously learning from user interactions and autonomously curating personalized content feeds, thus improving engagement while managing complexity through automation.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor user interactions with recommended segments. This feedback loop allows the system to refine its personalization algorithms and improve recommendations over time, balancing the need for personalization with manageable system complexity through iterative optimization.
Data Source
AI summary
A method includes receiving media content items. Using machine learning, audio segments are identified in the media content items based on analysis of content included in a corresponding media content item. Each of the identified audio segments is associated with automatically determined tags. A video clip is generated for a specific user using machine learning to automatically select, for the specific user, recommended audio segments from the identified audio segments based at least in part on prior user interactions of the specific user and the automatically determined tags of the identified audio segments, and identifying, for inclusion in the video clip, video segments of the media content items that correspond to the recommended audio segments. The video clip is provided for playback on a device associated with the specific user.