  Our network of acquaintances determines how we get exposed to ideas,
products, or cultural artworks (books, music, movies, etc.). Though this
principle is part of our common sense, little is known about the specific
pathways through which our peers influence our discovery processes and our
experience of the new. Here, we fill this gap by investigating a data set
containing the whole listening histories of a large, socially connected sample
of users from the online music platform \emph{Last.fm}. We demonstrate that
users exhibit highly heterogeneous discovery rates of new songs and artists and
that their social neighborhood significantly influences their behavior. More
explorative users tend to interact with peers more prone to explore new
content. We capture this phenomenology in a modeling scheme where users are
represented by random walkers exploring a graph of songs or artists and
interacting with each other through their social links. Even starting from a
uniform population of agents (no natural differences among the individuals),
our model predicts the emergence of strong heterogeneous exploration patterns,
with users clustered according to their musical tastes and propensity to
explore. We contend our approach can pave the way to a quantitative approach to
collective discovery processes.
