  Finding the music of the moment can often be a challenging problem, even for
well-versed music listeners. Musical tastes are constantly in flux, and the
problem of developing computational models for musical taste dynamics presents
a rich and nebulous problem space. A variety of factors all play some role in
determining preferences (e.g., popularity, musicological, social, geographical,
generational), and these factors vary across different listeners and contexts.
In this paper, we leverage a massive dataset on internet radio station creation
from a large music streaming company in order to develop computational models
of listener taste evolution. We delve deep into the complexities of this
domain, identifying some of the unique challenges that it presents, and develop
a model utilizing recurrent neural networks. We apply our model to the problem
of next station prediction and show that it not only outperforms several
baselines, but excels at long tail music personalization, particularly by
learning the long-term dependency structure of listener music preference
evolution.
