Playlists have become a significant part of our listening experience because of digital cloud-based services such as Spotify, Pandora, Apple Music, making playlist recommendation crucial to music services today. With an aim towards playlist discovery and recommendation, we leverage sequence-to-sequence modeling to learn a fixed-length representation of playlists in an unsupervised manner. We evaluate our work using a recommendation task, along with embedding-evaluation tasks, to study the extent to which semantic characteristics such as genre, song-order, etc. are captured by the playlist embeddings and how they can be leveraged for music recommendation.
CITATION STYLE
Papreja, P., Venkateswara, H., & Panchanathan, S. (2020). Representation, Exploration and Recommendation of Playlists. In Communications in Computer and Information Science (Vol. 1168 CCIS, pp. 543–550). Springer. https://doi.org/10.1007/978-3-030-43887-6_50
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