Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning
This addresses the time-consuming task for music curators to create personalized playlists, though it appears incremental as it builds on existing deep learning and reinforcement learning techniques.
The paper tackles the problem of automatic and personalized playlist generation by framing it as a language modeling task, using an attention RNN model refined with reinforcement learning, and demonstrates that it can generate coherent and flexible playlists based on user preferences.
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.