Inferring Music Selections for Casual Music Interaction

We present two novel music interaction systems developed for casual exploratory search. In casual search scenarios, users have an ill-defined information need and it is not clear how to determine relevance. We apply Bayesian inference using evidence of listening intent in these cases, allowing for a belief over a music collection to be inferred. The first system using this approach allows users to retrieve music by subjectively tapping a song’s rhythm. The second system enables users to browse their music collection using a radio-like interaction that spans from casual mood-setting through to explicit music selection. These systems embrace the uncertainty of the information need to infer the user’s intended music selection in casual music interactions.