Spotify ab (20240232257). SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT simplified abstract
Contents
SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT
Organization Name
Inventor(s)
Nicola Montecchio of Berlin (DE)
SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232257 titled 'SYSTEMS AND METHODS FOR DETECTING MISMATCHED CONTENT
Simplified Explanation: An electronic device uses a machine learning model to analyze attributes of media items, creates an acyclic graph based on pairwise similarity distances, clusters nodes representing media items, and modifies metadata to display media items in a user interface.
Key Features and Innovation:
- Utilizes machine learning model to determine pairwise similarity between media items
- Generates an acyclic graph based on similarity distances
- Clusters nodes representing media items for organization
- Modifies metadata to enhance user interface display
Potential Applications: This technology can be applied in:
- Content recommendation systems
- Media organization and management tools
- Personalized user interfaces for media consumption platforms
Problems Solved:
- Enhances organization and display of media items
- Improves user experience in navigating and accessing media content
- Enables personalized content recommendations based on similarity analysis
Benefits:
- Streamlines media item organization and display
- Enhances user engagement and satisfaction
- Facilitates efficient content discovery and recommendation processes
Commercial Applications: The technology can be utilized in:
- Streaming platforms
- Digital media libraries
- E-commerce websites for product recommendations based on media items
Prior Art: Readers can explore prior art related to media item clustering, metadata modification for user interfaces, and machine learning models in content recommendation systems.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for media item analysis, user interface design principles for content display, and clustering techniques for organizing digital content.
Questions about Media Item Clustering: 1. How does this technology improve user experience in media consumption? 2. What are the potential implications of this technology in content recommendation systems?
Question 1: How does this technology improve user experience in media consumption?
Answer 1: This technology enhances user experience by organizing media items effectively, providing personalized recommendations, and improving the overall navigation and accessibility of content for users.
Question 2: What are the potential implications of this technology in content recommendation systems?
Answer 2: The technology can significantly enhance the accuracy and relevance of content recommendations, leading to increased user engagement, satisfaction, and potentially higher retention rates for platforms implementing this technology.
Original Abstract Submitted
an electronic device obtains a plurality of media items, including, for each media item in the plurality, a set of attributes of the media item. the device provides the set of attributes for each media item of the plurality of media items to a machine learning model that is trained to determine a pairwise similarity between respective media items in the plurality of media items and generates an acyclic graph of an output of the machine learning model that is trained to determine pairwise similarity distances between respective media items in the plurality of media items. the device clusters nodes of the acyclic graph, each node corresponding to a media item. based on the clustering, the electronic device modifies metadata associated with a first media item in a first cluster and displays a representation of the first media item in a user interface according to the modified metadata.
