Recommending Music Curators: A Neural Style-Aware Approach

Wang, Jianling; Caverlee, James

We propose a framework for personalized music curator recommendation to connect users with curators who have matching curation style. Three unique features of the proposed framework are: (i) models of curation style to capture the coverage of music and curator’s individual style in assigning tracks to playlists; (ii) a curation-based embedding approach to capture inter-track agreement, beyond the audio features, resulting in models of music tracks that pair well together; and (iii) a novel neural pairwise ranking model for personalized music curator recommendation that naturally incorporates both curator style models and track embeddings. Experiments over a Spotify dataset show significant improvements in precision, recall, and F1 versus state-of-the-art.