Creation Attribution for Effect Trend Detection in Media Content
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Solution Overview
Problem
Existing content sharing services lack the ability to identify and present effect trends that inspire users, as they do not effectively utilize creation attribution to connect media content items with shared video and audio effects.
Innovation Solution
A computing system identifies effect trends by determining media content items with creation attributes, where one media content item inspires the creation of similar effects in another item within a predefined period, using machine learning models to cluster items with similar concepts and effects, and presents these trends to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content sharing services present media content items without analyzing creation attribution, then the system complexity is reduced, but the ability to identify inspiring effect trends is lost
Solution Approach 1:
The system performs preliminary analysis by determining creation attribution for media content items before presenting them to users. This involves analyzing whether a first media content item inspired the creation of a second media content item with similar effects, and pre-processing this information to enable subsequent effect trend identification without adding complexity during the content delivery phase
Solution Approach 2:
The system introduces creation attribution as an intermediary data structure that connects media content items through their inspirational relationships. This intermediary layer enables the system to track and identify effect trends by analyzing the chain of inspiration between content items, without requiring direct complex analysis during content presentation
2Measurement precision
If the system analyzes creation attribution to identify effect trends, then the identification accuracy is improved, but the processing time increases
Solution Approach 1:
The system applies partial action by focusing analysis only on media content items that have potential inspirational relationships, rather than analyzing all content items uniformly. It determines creation attribution selectively based on whether specific conditions are met (e.g., similar effects, temporal proximity), reducing unnecessary processing while maintaining identification accuracy
3Stability of the object's composition
If the system clusters media content items with similar effects and concepts, then the coherence of effect trends is improved, but the computational resources required increase
Solution Approach 1:
The system applies local quality by clustering media content items based on their specific effect attributes and conceptual characteristics rather than treating all items uniformly. It identifies groups of items with shared video effects, audio effects, and concepts, creating localized coherent clusters that maintain compositional stability while reducing the need for exhaustive comparison of all items
Data Source
AI summary
In one example, a computing system comprises a memory that stores instructions, and processing circuitry that executes the instructions to: identify, from a plurality of media content items, a seed media content item with one or more effect attributes indicating one or more effects used in at least the seed media content item, determine whether the seed media content item is associated with a creation attribute indicating one or more users created one or more other media content items including the one or more effects within a predefined period of time after the seed media content item was watched by the one or more users, responsive to determining the seed media content item is associated with the creation attribute, identify a set of media content items with the one or more effect attributes, and output an indication of an effect trend including the set of media content items.