Recommending for the Audience: Tailoring a Live Concert Program to the Music Preferences of the Listeners

How can we use recommender technologies to personalize a concert program to the music preferences of the audience, even if that audience is not very familiar with the music genre of the performance? We present the results of two use cases in which we used a group recommendation approach to tailor a live concert program (one opera singer and one choir concert) to the user profiles of the audience. Using Gaussian mixture modeling, we matched musical attributes of the songs from the performance list of the artist to the (Spotify) user profiles of concert visitors. This allowed us to generate a matching concert program for the audience as a whole, as well as a personalized ranking of the songs in the program for each user. During the concert, we tested how much the audience enjoyed the songs and if the predicted ranking of the songs matched their actual preferences, using an app that would show live predictions and ask for their (user) experience. The results show that our algorithm was able to predict user preferences and rankings for songs performed during the concert. Gaussian mixture modeling on audio features seems to be a feasible tool to tailor a concert program to the musical preferences of the audience, even for music outside of listeners' normal music genres and preferences.