Automated Playlist Generation via Event Database Search
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Solution Overview
Problem
Users face challenges in manually creating playlists for events, especially multi-act or multi-day events, which is time-consuming and tedious, leading to a lack of customized music experiences for users interested in upcoming events.
Innovation Solution
A system and method for automatically generating playlists based on user interest data, including calendar entries, search history, and social network content, by searching event and media databases to identify relevant songs and videos associated with the user's interests, with the ability to access and purchase the media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If playlists are manually compiled by users or other persons, then the playlists can be customized to user preferences, but the process is time-consuming and tedious
Solution Approach 1:
The system performs preliminary actions by automatically searching event databases and media databases to identify relevant content before the user needs it. Event data is pre-collected including performer information, setlists, and media associations. When a user shows interest in an event, the playlist is already prepared or can be quickly generated from pre-processed data.
Solution Approach 2:
The system enables self-service by automatically generating playlists based on user interest data without requiring manual compilation. The system monitors user interactions with event information and autonomously creates customized playlists by matching event content with media database entries, allowing users to receive personalized playlists without investing time in manual creation.
2Productivity
If playlists are automatically generated based on user interest data, then the playlist creation time is reduced, but the system complexity increases
Solution Approach 1:
The system segments the playlist generation process into distinct modules: an event database module that stores event information and performer data, a media database module that contains music and video content, a user interest tracking module that monitors user interactions, and a playlist generation module that synthesizes the data. This segmentation allows each component to operate independently and efficiently, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system introduces intermediary components including event data structures that bridge user interests and media content, and playlist logic that mediates between database queries and final playlist assembly. These intermediaries simplify the complexity by providing standardized interfaces and data transformation layers between different system components.
3Measurement precision
If the system searches event and media databases to identify relevant content, then the playlist accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary indexing and categorization of event data and media content during data ingestion. Event entries are pre-tagged with performer information, genres, and associated media identifiers. Media database entries are pre-indexed by artist, album, genre, and other metadata. This preliminary processing enables rapid accurate matching during playlist generation without requiring exhaustive searches at query time.
Data Source
AI summary
A system and method of generating a playlist includes searching an event database having multiple events stored therein. One of the events is identified as corresponding to a user's interest data. A song database is searched. The song database includes at least one song corresponding to the identified event. A playlist corresponding to the identified event is generated. The playlist includes a list of at least one song corresponding to the identified event and a logical link to a recording of the at least one song. Access to the playlist can then be provided to the user.