US20250358479
2025-11-20
Electricity
H04N21/4667
The invention focuses on real-time identification of media trends on a content-sharing platform. By generating embeddings that represent features of a media item during a specific time window, the system assesses the similarity between these features and those of other media items. If the similarity meets predefined criteria, these items are recognized as part of an emerging trend. This trend information is then provided to users in real-time, enhancing their engagement with current platform dynamics.
The method involves creating embeddings to capture various features of media items, such as audiovisual and textual elements. These embeddings help determine the degree of similarity between media items to identify trends. The system considers user engagement data to further validate trends, utilizing an anomaly engine to track fluctuations in user interactions. This approach ensures that emerging trends are accurately detected and communicated to users promptly.
User engagement plays a crucial role in trend identification. The system evaluates engagement data against criteria to confirm trend status, using aggregated metrics to gauge user interest levels. The process includes comparing new media items with existing ones to foresee future trends, ensuring that users are always informed about popular content they might want to interact with or contribute to.
These features are analyzed through embeddings that undergo concatenation and attention pooling operations, resulting in comprehensive data used for trend detection.
The system enhances user experience by updating the client device's interface with UI elements indicating emerging trends. When users interact with these elements, they gain access to content related to the trend. This interactive approach not only keeps users engaged but also encourages them to participate in ongoing trends by contributing their media items.