Recsys Challenge 2018: Automatic Music Playlist Continuation


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.


April 2022 | The Web Conference (WWW)

Sequential Recommendation via Stochastic Self-Attention

Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu

April 2022 | The Web Conference (WWW)

Using Survival Models to Estimate Long-Term Engagement in Online Experiments

Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara

April 2022 | The Web Conference (WWW)

Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations

Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catie Edrwards, Ben Carterette, Mounia Lalmas