Music Playlist Title Generation Using Artist Information
- URL: http://arxiv.org/abs/2301.08145v1
- Date: Sat, 14 Jan 2023 00:19:39 GMT
- Title: Music Playlist Title Generation Using Artist Information
- Authors: Haven Kim, SeungHeon Doh, Junwon Lee, Juhan Nam
- Abstract summary: We present an encoder-decoder model that generates a playlist title from a sequence of music tracks.
Comparing the track IDs and artist IDs as input sequences, we show that the artist-based approach significantly enhances the performance in terms of word overlap, semantic relevance, and diversity.
- Score: 4.201869316472344
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automatically generating or captioning music playlist titles given a set of
tracks is of significant interest in music streaming services as customized
playlists are widely used in personalized music recommendation, and
well-composed text titles attract users and help their music discovery. We
present an encoder-decoder model that generates a playlist title from a
sequence of music tracks. While previous work takes track IDs as tokenized
input for playlist title generation, we use artist IDs corresponding to the
tracks to mitigate the issue from the long-tail distribution of tracks included
in the playlist dataset. Also, we introduce a chronological data split method
to deal with newly-released tracks in real-world scenarios. Comparing the track
IDs and artist IDs as input sequences, we show that the artist-based approach
significantly enhances the performance in terms of word overlap, semantic
relevance, and diversity.
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