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The automated generation of music playlists can be naturally regarded as asequential task, where a recommender system suggests a stream of songs thatconstitute a listening session. In order to predict the next song in aplaylist, some of the playlist models proposed so far consider the current andprevious songs in the playlist (i.e., the song context) and possibly the orderof the songs in the playlist. We investigate the impact of the song context andthe song order on next-song recommendations by conducting dedicated off-lineexperiments on two datasets of hand-curated music playlists. Firstly, wecompare three playlist models, each able to consider a different song contextlength: a popularity-based model, a song-based Collaborative Filtering (CF)model and a Recurrent-Neural-Network-based model (RNN). We also consider amodel that predicts next songs at random as a reference. Secondly, we challengethe RNN model (the only model from the first experiment able to consider thesong order) by manipulating the order of songs within playlists. Our resultsindicate that the song context has a positive impact on the quality ofnext-song recommendations, even though this effect can be masked by the biastowards very popular songs. Furthermore, in our experiments the song order doesnot appear as a crucial variable to predict better next-song recommendations.

The Importance of Song Context and Song Order in Automated Music Playlist Generation