Media Trend Visualization Using Embedding Vectors
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Solution Overview
Problem
Existing content sharing platforms face challenges in efficiently detecting and visualizing media trends among a large volume of media items due to resource-intensive and inaccurate methods of identifying common audiovisual and metadata features, leading to increased computing resources and user inefficiency in accessing relevant content.
Innovation Solution
A system that utilizes audiovisual and textual embeddings to detect media trends, providing users with graphical interfaces to access and create content associated with detected trends, and allowing creators to control the distribution of their media items within those trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect media trends and identify common audiovisual features, then comprehensive analysis can be achieved, but computing resource consumption increases and processing time extends
Solution Approach 1:
The patent replaces traditional mechanical computing methods with neural network-based embeddings. Audiovisual features are transformed into compact embedding vectors that capture essential characteristics without requiring exhaustive computational analysis of raw media data, significantly reducing processing resources while maintaining detection accuracy
Solution Approach 2:
The system changes the parameter representation from raw audiovisual data to compressed embedding vectors. By transforming high-dimensional media features into lower-dimensional semantic representations, the system achieves efficient comparison and trend detection with reduced computational complexity
2Loss of information
If comprehensive media analysis is performed to identify all media items associated with trends, then complete trend visualization is achieved, but user latency increases
Solution Approach 1:
The system performs preliminary analysis by pre-computing embeddings for all media items and maintaining them in an accessible format. When a user queries for trend information, the system can rapidly retrieve and compare pre-processed embeddings without performing full media analysis at query time, reducing user latency while preserving complete trend information
Solution Approach 2:
The patent creates compact embedding copies of media features that can be quickly retrieved and compared. These embedding representations serve as efficient proxies for the original media items, enabling fast trend identification and visualization without requiring access to the full media data during user interactions
3Measurement precision
If detailed audiovisual feature extraction is performed on all media items, then accurate trend identification is achieved, but processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical feature extraction pipelines with neural network-based embedding models. These models automatically learn and extract relevant audiovisual features through trained representations, eliminating the need for manual feature engineering and reducing system complexity while maintaining or improving extraction accuracy
Data Source
AI summary
Methods and systems for visualizing media trends at a content sharing platform are provided herein. A request of a client device is received for media items of a platform. A graphical user interface (GUI) including audiovisual content of one or more media items and an indication that the media items are associated with a media trend of the platform is provided for presentation on the client device. The media trend is associated with a set of media items, where content of the set of media items share common audiovisual characteristics in accordance with a concept of the media trend A user interaction with a GUI element associated with the media items is detected. Responsive to the detection, an additional GUI is provided for presentation on to the client device, the additional GUI including audiovisual content of a set of media items associated with the media trend.