  Songs can be well arranged by professional music curators to form a riveting
playlist that creates engaging listening experiences. However, it is
time-consuming for curators to timely rearrange these playlists for fitting
trends in future. By exploiting the techniques of deep learning and
reinforcement learning, in this paper, we consider music playlist generation as
a language modeling problem and solve it by the proposed attention language
model with policy gradient. We develop a systematic and interactive approach so
that the resulting playlists can be tuned flexibly according to user
preferences. Considering a playlist as a sequence of words, we first train our
attention RNN language model on baseline recommended playlists. By optimizing
suitable imposed reward functions, the model is thus refined for corresponding
preferences. The experimental results demonstrate that our approach not only
generates coherent playlists automatically but is also able to flexibly
recommend personalized playlists for diversity, novelty and freshness.
