Dynamic Playlist Generation via User Listening Data and Host Filters
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Solution Overview
Problem
Establishments and event hosts often play music that does not match the musical preferences of individuals present in a room or area, as they are unaware of the attendees' preferences.
Innovation Solution
A method that identifies users within a selected area, retrieves their music listening data, builds a playlist based on this data, filters it according to host preferences, and plays the filtered playlist, allowing for dynamic selection and playback of user-preferred music.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If music is selected by the host or played by a music channel, then the host's control over the music environment is maintained, but the music may not match the musical preferences of the individuals present in the room or area
Solution Approach 1:
The system collects music listening data from users' electronic devices as feedback about their preferences. This feedback is processed to identify commonly preferred genres and artists, which then informs the automated playlist generation. The host preferences also serve as feedback to filter and refine the final playlist, ensuring both user preference alignment and host control.
Solution Approach 2:
The system enables users to effectively select their own music preferences by automatically analyzing their listening data. Each user's music listening data is retrieved and processed to identify their preferred genres and artists, allowing the system to self-adjust the playlist based on aggregated user preferences without requiring manual input from each individual.
2Adaptability or versatility
If automated playlist generation based on user data is implemented, then music preference matching is improved, but system complexity increases
Solution Approach 1:
The system uses a multi-functional approach where a single automated process handles multiple tasks: retrieving music listening data from multiple users, analyzing the data to identify commonly preferred genres and artists, generating the playlist, and applying host preference filters. This universal process eliminates the need for separate manual configuration steps for each function.
Solution Approach 2:
The system introduces an intermediary automated playlist generation process that mediates between raw user listening data and the final music playback. This intermediary layer processes and synthesizes user preferences into a coherent playlist while incorporating host preferences as a filtering mechanism, simplifying the overall system architecture by centralizing the decision-making logic.
3Ease of operation
If music is selected without considering user preferences, then the establishment's objectives are maintained, but user satisfaction and engagement decrease
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and analyzing users' music listening data before playlist generation. This advance preparation of user preference information allows the system to pre-identify commonly preferred genres and artists, so that when the playlist is generated, it already reflects user preferences without requiring hosts to manually survey or ask users about their tastes.
Solution Approach 2:
The system dynamically changes the playlist parameters (genre, artist, tempo, mood) based on aggregated user listening data. By analyzing patterns in user music consumption, the system adjusts these parameters to reflect collective user preferences while still allowing hosts to apply their own preference filters, thus adapting the music selection to user characteristics without losing host control.
Data Source
AI summary
A method for playing music includes identifying a plurality of users of a corresponding plurality of electronic devices that are currently located within a selected area, retrieving music listening data for the plurality of users, building a playlist for the selected area based on the music listening data, filtering the playlist according to at least one host preference to produce a filtered playlist, and playing the filtered playlist within the selected area. The selected area may be a geo-fenced area. Examples of host preferences include genre, artist, tempo, mood and demographic. The playlist may include songs that are commonly selected by the plurality of users or conform to genres commonly preferred by the plurality of users. A corresponding system and computer program product for executing the above method are also disclosed herein.