A model for evaluating popularity and semantic information variations in radio listening sessions

Listening to music radios is an activity that since the 20th century is part of the cultural habits for people all over the world. While in the case of analog radios DJs are in charge of selecting the music to be broadcasted, nowadays recommender systems analyzing users’ behaviours can automatically generate radios tailored to users’ musical taste. Nonetheless, in both cases listening sessions do not depend on the listener choices, but on a set of external recommendations received. In this preliminary study, we propose a model for estimating features’ variation during listening sessions, comparing different scenarios, namely analog radios, personalized and not-personalized streaming radios. In particular, we focus on the analysis of track popularity and semantic information, features well-established in the Music Information Retrieval literature. The presented model aims to quantify the possible impact of the sessions’ variation on the user listening experience.