Current music recommender systems typically act in a greedy fashion by
recommending songs with the highest user ratings. Greedy recommendation,
however, is suboptimal over the long term: it does not actively gather
information on user preferences and fails to recommend novel songs that are
potentially interesting. A successful recommender system must balance the needs
to explore user preferences and to exploit this information for recommendation.
This paper presents a new approach to music recommendation by formulating this
exploration-exploitation trade-off as a reinforcement learning task called the
multi-armed bandit. To learn user preferences, it uses a Bayesian model, which
accounts for both audio content and the novelty of recommendations. A
piecewise-linear approximation to the model and a variational inference
algorithm are employed to speed up Bayesian inference. One additional benefit
of our approach is a single unified model for both music recommendation and
playlist generation. Both simulation results and a user study indicate strong
potential for the new approach.

本文提出了一种基于强化学习和贝叶斯模型的音乐推荐和播放列表生成方法，探讨了探索和利用用户偏好的平衡，并表明这种新方法具有很强的潜力。