Andreu Vall, M. Quadrana, Markus Schedl, Gerhard Widmer,
"The Importance of Song Context and Song Order in Automated Music Playlist Generation"
: Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC 2018) and 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018), 2019
The Importance of Song Context and Song Order in Automated Music Playlist Generation
Sprache des Titels:
Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC 2018) and 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018)
The automated generation of music playlists can be naturally
regarded as a sequential task, where a recommender system suggests
a stream of songs that constitute a listening session. In order to
predict the next song in a playlist, some of the playlist models
proposed so far consider the current and previous songs in the
playlist (i.e., the song context) and possibly the order of the songs in
the playlist. We investigate the impact of the song context and the
song order on next-song recommendations by conducting dedicated
off-line experiments on two datasets of hand-curated music playlists.
Firstly, we compare three playlist models, each able to consider a
different song context length: a popularity-based model, a songbased Collaborative Filtering (CF) model and a Recurrent-NeuralNetwork-based model (RNN). We also consider a model that predicts
next songs at random as a reference. Secondly, we challenge the
RNN model (the only model from the first experiment able to
consider the song order) by manipulating the order of songs within
playlists. Our results indicate that the song context has a positive
impact on the quality of next-song recommendations, even though
this effect can be masked by the bias towards very popular songs.
Furthermore, in our experiments the song order does not appear as a
crucial variable to predict better next-song recommendations.