Crowd-Sourced Media Segmentation via User Feedback
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Solution Overview
Problem
Existing systems require significant time and computing resources for media item creators to identify and highlight interesting content segments, which can be inefficient and wasteful if users do not find these segments interesting over time.
Innovation Solution
A crowd source-based time marking system where users can indicate interesting content segments of media items, allowing the platform to determine bookmarks for these segments based on collective user feedback, thereby reducing the need for creators to manually highlight content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If creators manually identify and highlight interesting content segments, then content segmentation can be performed, but significant time and computing resources are consumed
Solution Approach 1:
The system enables users to automatically identify and mark interesting content segments through crowd-sourced time marking. Instead of relying on creators to manually segment content, the platform leverages collective user feedback to generate bookmarks and timestamps, making the system self-serve the content segmentation function
Solution Approach 2:
The system collects feedback from multiple users about which content segments they find interesting, then uses this aggregated feedback to automatically determine bookmarks and timestamps. This feedback loop replaces manual creator effort with automated crowd-sourced intelligence
2Productivity
If creators manually highlight interesting content segments, then content can be segmented, but resources are wasted if users do not find these segments interesting
Solution Approach 1:
The platform automatically performs content segmentation by aggregating user feedback, eliminating the need for creators to invest computing resources in manual segmentation. The system serves itself by using crowd-sourced data to generate bookmarks without creator intervention
Solution Approach 2:
Instead of requiring complete manual segmentation by creators, the system uses partial user feedback from multiple users to collectively achieve accurate content segmentation. The aggregated partial actions of many users produce better results than single-creator manual effort
3Loss of information
If the entire media item is consumed to access interesting segments, then complete content is available, but time and bandwidth are wasted
Solution Approach 1:
The system extracts and isolates only the interesting content segments that users want to access, rather than requiring consumption of the entire media item. Bookmarks and timestamps extract key segments for direct access, eliminating unnecessary content consumption
Solution Approach 2:
The media item is divided into discrete content segments with bookmarks marking interesting portions. Users can access specific segments directly through timestamps without consuming the entire media item, enabling efficient targeted content access
Data Source
AI summary
Methods and systems for crowd source-based time marking of media items at a platform are provided herein. A media item is provided to first client devices associated with first users of a platform. An indication is received from each of the first client devices of an interesting content segment of the media item as selected by a first user of the platform. At least one content segment of the media item to be associated with a bookmark for a timeline of the media item is determined. The at least one content segment is determined in view of the indication received from each of the first client devices. The media item and the indication of the bookmark is provided to a second client device for presentation to a second user of the platform.