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 ...
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 impacts of the sessions’
variation on the user listening experience.
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