Dynamic Media Station Library Curation via Pre-computed Scoring
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Solution Overview
Problem
The existing technologies face challenges in efficiently curating and managing large media collections, such as audio and video recordings, to create highly-focused, criteria-based dynamic station libraries.
Innovation Solution
The system enables users to create and edit station libraries by providing a user interface and supporting functionality that filters media content catalogs based on user-defined criteria, generates similarity and popularity scores, and selects top-ranked tracks to build dynamic station libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system processes massive media catalogs (e.g., 30 million tracks), then the quantity of available content increases, but the time required to search and curate content increases
Solution Approach 1:
The system pre-computes and stores metadata, similarity scores, and rankings for media content before user queries are received. This preliminary processing enables fast retrieval and curation of content without requiring real-time analysis of the entire massive catalog, thus reducing curation time while maintaining access to large quantities of content.
Solution Approach 2:
The system introduces an intermediary indexing and filtering layer that mediates between the user's curation needs and the massive media catalog. This intermediary structure organizes content by multiple criteria (genre, artist, similarity, popularity) and enables efficient querying without requiring direct processing of all 30 million tracks, thereby reducing time loss.
2Measurement precision
If the system provides comprehensive search criteria and filtering options, then the precision of content selection improves, but the device complexity increases
Solution Approach 1:
The system segments the complex curation task into multiple independent filtering dimensions (genre, artist, era, similarity score, popularity score). Each dimension can be filtered separately through a hierarchical tree structure, making the overall complex selection process manageable and precise without requiring a monolithic complex system.
Solution Approach 2:
The system allows users to dynamically adjust filtering parameters (minimum/maximum similarity scores, popularity ranges, genre selections) to achieve precise content selection. By changing these parameters, the system can precisely control the output without requiring complex custom logic, thus achieving high precision with manageable complexity.
3Adaptability or versatility
If the system generates dynamic station libraries with multiple criteria, then the adaptability of content curation improves, but the computational resources required increase
Solution Approach 1:
The system pre-computes similarity scores and rankings for media content based on multiple criteria and stores them in an indexed structure. When users need dynamic station libraries, the system queries this pre-processed data rather than performing complex computations in real-time, enabling high adaptability with reduced computational resource consumption.
Solution Approach 2:
The system replaces heavy real-time computational analysis with efficient data querying and filtering operations on pre-processed data structures. This substitution allows the system to generate adaptive station libraries with multiple criteria while minimizing the computational resources required during actual curation operations.
Data Source
AI summary
Systems and methods are provided for filtering at least one media content catalog based on criteria for a station library to generate a first list of candidate tracks for the station library, combining a similarity score and a popularity score for each track of the first list of candidate tracks to generate a total score for each track of the first list of candidate tracks, generating a list of top ranked tracks for the first genre, and returning the list of top ranked tracks of the first genre as part of the station library.