C.W. Chen, Markus Schedl, Paul Lamere, H. Zamani,
"RecSys Challenge 2018: Automatic Music Playlist Continuation"
: Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018), 10-2018
Original Titel:
RecSys Challenge 2018: Automatic Music Playlist Continuation
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018)
Original Kurzfassung:
The ACM Recommender Systems Challenge 2018 focused on automatic music playlist continuation, which is a form of the more
general task of sequential recommendation. Given a playlist of arbitrary length, the challenge was to recommend up to 500 tracks
that fit the target characteristics of the original playlist. For the
Challenge, Spotify released a dataset of one million user-created
playlists, along with associated metadata. Participants could submit
their approaches in two tracks, i.e., main and creative tracks, where
the former allowed teams to use solely the provided dataset and the
latter allowed them to exploit publicly available external data too.
In total, 113 teams submitted 1,228 runs in the main track; 33 teams
submitted 239 runs in the creative track. The highest performing
team in the main track achieved an R-precision of 0.2241, an NDCG
of 0.3946, and an average number of recommended songs clicks of
1.784. In the creative track, an R-precision of 0.2233, an NDCG of
0.3939, and a click rate of 1.785 was realized by the best team.