Music Playlist Recommendation via Preference Embedding

Chih-Ming Chen, Chun-Yao Yang, Chih-Chun Hsia, Yian Chen, Ming-Feng Tsai
2016 ACM Conference on Recommender Systems  
Music playlists usually contain some particular musical styles or atmospheres in which users would like to be involved. Music streaming services, such as Spotify, Apple Music, and KKBOX, even allow users to edit and listen to playlists online. While there have been some well-known methods that can nicely model the preference between users and songs, little has been done in the literature to recommend music playlists, each of which can be considered as a set of many individual songs, to users.
more » ... the light of this, this paper proposes a preference embedding based on a user-song-playlist graph to learn the preference representations of these three entities. After the embedding process, we then use the learned representations to perform the task of playlist recommendation. Experiments conducted on a real-world dataset show that the proposed embedding method outperforms the baseline of popularity; in addition, we also make a comparison with DeepWalk and LINE for the recommendation task, and the results show that the proposed method can stand comparison with the two state-of-the-art graph embedding techniques.
dblp:conf/recsys/ChenYHCT16 fatcat:dcmgqy7omvhtldqqkvqyirwqsi