A Music Discovery Service powered by the music preferences of people around you

Submitted by George Krasadakis
Signed on Ethereum on 9/4/2019
An app for music fans which seamlessly discovers music preferences of those in proximity with you in order to discover and discuss music liked from people around you. Users benefit by discovering music easily in a social context when you are meeting people socially, professionally or randomly.
Naturally, discover music by accessing the most popular & recently played songs in reference to a particular social arrangement

Assuming a particular social arrangement (a class in a University, customers in a Bar/Movie theater, friends having a coffee etc.); this app scans the connected music services of each person in the arrangement (for instance Spotify accounts, YouTube music content, etc.) and creates a consolidated list of recently and most frequently played songs for the members of this social arrangement.

The app recommends music to each user, inspired by the particular song/artist preferences of the persons in the arrangement. A log of these recommendations empowers an ongoing, social-driven music discovery engine (powered both by people I know and strangers which happened to be in the same place as well). The offline experience may also support naming the social arrangement, exposing patterns related to it (i.e. ad-hoc or repeating, social or professional, etc.); could also support automatic conversion to a group, with suggestions to join and exchange opinions and preferences on music and more.

The system can further present the history of the social arrangements the user has joined, enriched with music preferences, trends, gaps, recommendations, and statistics. The public website experience presents musical preferences on maps, classifications schemes (locations, geography, profession, social, party, family, wedding …) along with interesting metadata and stats enabling alternative music discovery scenarios.

Stores, concert halls, restaurants, cafes, clubs can use this to know what music their real customers prefer — for instance a cafe could use this system to instantly get the best possible mix of songs for the particular audience in the store at a particular moment; then automatically use a streaming music service to serve these playlist to the customers.

Another interesting scenario is the discovery of places based on musical preferences of the people physically appearing in the place; users can discover places based on the musical preferences of other people present in the specific location/ place.