Playlist Generation via Multi-Channel Score Combination
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Solution Overview
Problem
Users face limitations in selecting music due to the need for manual selection or subscription-based services, which do not provide personalized or customized listening experiences tailored to their preferences, location, or context.
Innovation Solution
A computer-implemented method and system that automatically generates playlists by combining inputs from various sources, using a server system to receive a media input seed, obtain multiple channels of media content objects, calculate combination scores, rank media content objects, and select one for transmission based on user preferences, demographic information, and contextual factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select songs or create playlists, then they can access music from their personal collection, but the selection process requires significant time and effort
Solution Approach 1:
The system performs automatic playlist generation and song selection without requiring user intervention. The server autonomously processes user preferences, demographic information, and contextual factors to generate personalized playlists and select individual songs, eliminating the manual effort previously required from users.
Solution Approach 2:
The system pre-processes user profiles, preferences, and contextual data before actual music selection is needed. By maintaining pre-computed user profiles and preference models, the system can rapidly generate personalized playlists and select songs without requiring time-consuming analysis at the moment of selection.
2Quantity of substance
If users subscribe to online music services, then they gain access to larger music libraries, but they still must manually identify and select songs for playback
Solution Approach 1:
The system automatically identifies and selects songs from the music library based on user profiles and preferences, eliminating the manual song identification step. The server autonomously queries the music database, evaluates songs against user criteria, and generates personalized playlists without user intervention.
Solution Approach 2:
The system introduces an intelligent recommendation engine as an intermediary between the user and the music library. This mediator automatically matches user preferences with appropriate songs, translating user profiles into concrete music selections without requiring users to manually search or identify songs.
3Ease of operation
If broadcast radio stations provide automated programming, then users do not need to select songs manually, but the listening experience is not customized for individual users
Solution Approach 1:
The system tailors the music selection to each individual user by incorporating personal preferences, demographic information, and contextual factors specific to that user. Rather than applying a uniform programming approach, the system generates unique personalized playlists for each user based on their local characteristics and preferences.
Solution Approach 2:
The system pre-processes and stores detailed user profiles containing preferences, demographic data, and contextual information before music selection is needed. This preliminary customization of user profiles enables the automated system to quickly generate personalized playlists without sacrificing individualization.
4Measurement precision
If the system combines multiple input channels to generate playlists, then personalization accuracy improves, but the system complexity increases
Solution Approach 1:
The system divides the complex input processing into distinct modular channels, each handling specific types of data (user preferences, demographic information, contextual factors). By segmenting the input processing into separate manageable channels, the system can combine multiple data sources without creating unmanageable complexity.
Solution Approach 2:
The system employs a universal scoring mechanism that processes multiple different input channels through a common evaluation framework. The combination score calculation uses a consistent mathematical approach across all input types, allowing diverse data sources to be integrated without requiring complex channel-specific processing logic.
Data Source
AI summary
Systems, methods, and computer readable storage mediums are provided for selecting a media content object for a user using a combination of inputs. A media input seed associated with a user is obtained. A plurality of channels of media content objects is obtained. At least one of the plurality of channels is associated with the media input seed. Also, in some embodiments, each media content object of each of those channels has a score specific to that channel. A combination score for a respective media content object is calculated based at least in part on that respective media content object's channel specific score for each of at least two of the plurality of channels. Then at least some of the media content objects are ranked based at least in part on their respective combination scores. Finally, at least one ranked media content object is then selected for transmission.