TY - GEN
T1 - Representation, Exploration and Recommendation of Playlists
AU - Papreja, Piyush
AU - Venkateswara, Hemanth
AU - Panchanathan, Sethuraman
N1 - Funding Information:
The authors thank ASU, Adidas, and the National Science Foundation for their funding support. This material is partially based upon work supported by Adidas and by the National Science Foundation under Grant No. 1828010.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Playlists
KW - Recommendation
KW - Sequence-to-sequence
UR - http://www.scopus.com/inward/record.url?scp=85083664827&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083664827&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-43887-6_50
DO - 10.1007/978-3-030-43887-6_50
M3 - Conference contribution
AN - SCOPUS:85083664827
SN - 9783030438869
T3 - Communications in Computer and Information Science
SP - 543
EP - 550
BT - Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
A2 - Cellier, Peggy
A2 - Driessens, Kurt
PB - Springer
T2 - 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Y2 - 16 September 2019 through 20 September 2019
ER -