  In this paper, we aim to solve the automatic playlist continuation (APC)
problem by modeling complex interactions among users, playlists, and songs
using only their interaction data. Prior methods mainly rely on dot product to
account for similarities, which is not ideal as dot product is not metric
learning, so it does not convey the important inequality property. Based on
this observation, we propose three novel deep learning approaches that utilize
Mahalanobis distance. Our first approach uses user-playlist-song interactions,
and combines Mahalanobis distance scores between (i) a target user and a target
song, and (ii) between a target playlist and the target song to account for
both the user's preference and the playlist's theme. Our second approach
measures song-song similarities by considering Mahalanobis distance scores
between the target song and each member song (i.e., existing song) in the
target playlist. The contribution of each distance score is measured by our
proposed memory metric-based attention mechanism. In the third approach, we
fuse the two previous models into a unified model to further enhance their
performance. In addition, we adopt and customize Adversarial Personalized
Ranking (APR) for our three approaches to further improve their robustness and
predictive capabilities. Through extensive experiments, we show that our
proposed models outperform eight state-of-the-art models in two large-scale
real-world datasets.
